طبقه بندی

Autism is classified as a developmental disorder and primarily disrupts social interactions and communication. This disorder has no definitive treatment, making early diagnosis crucial for mitigating its effects. The purpose of this study is to identify autistic individuals based on the recorded information of their walking pattern by Kinect sensor.

In this research, the machine learning method was employed to identify autistic individuals based on recorded joint position data during walking, recorded by the Kinect sensor. First, a group of statistical features was extracted from the Kinect data, which included joint positions and the angles between them. Then, the extracted features were evaluated using the statistical test of analysis of variance, and the optimal features were selected. Finally, classification was performed by decision tree classifier.

In this research, the classification of healthy and autistic individuals was done by the decision tree classification and 42 optimal features selected based on statistical analysis, and the accuracy of classification was 85%. The sensitivity and specificity obtained in this classification are 88 and 82%, respectively.

According to the classification results, this research was able to achieve acceptable accuracy by using the low dimension feature vector obtained by statistical analysis. This research, shows autistic individuals can be classified from healthy people only by having the position of several joints. It is suggested researches in future, using this method for measurement the recovery rate or control autism in patient after performing treatment methods.

Based on the kinesiopathologic concept of movement, the corrective strategies targeting the altered movement and postural patterns can prevent the onset of musculoskeletal pains. Before planning any measure, the specialists need an accurate identification of movement and postural impairments predisposing the individual to a specific musculoskeletal pain. There are conflicting results regarding the effectiveness of a specific corrective intervention for the prevention of low back pain (LBP). There is a classification system named movement system impairment (MSI) developed by Sahrmann. No previous study has been conducted to validate this system for preventive measures of LBP. Therefore, this study aimed to assess the construct validity of this model in females susceptible to LBP. 

 In this study, 250 females aged 18-75 with transient LBP (based on a VAS score >10 during a 30-min prolonged standing procedure), were examined by two corrective exercise specialists. The principal component analysis (PCA) was used to derive proposed LBP categories. Factor loadings in the component matrix were assessed to identify the factors for each component. Five configured components were compared with the five LBP categories proposed by the MSI 

The five derived components were extension-rotation, flexion-rotation, flexion, rotation and extension-rotation. These five extracted components explained 87% of the total cumulative variance, equivalent to five MSI-based LBP categories.

 The MSI is a valid classification tool for preventive measures against LBP in females. Specialists can use this model for identification of females susceptible to movement and postural impairments, and take a positive step towards preventing LBP by modifying these impairments before the onset of pain.

In this study, a method for automatic prediction of the survival rate of patients with glioblastoma tumor based on machine learning methods and MRI images is presented.

The data set used in this study is the BraTS 2017 database with 163 samples. Each sample of database images has four different imaging modalities as well as information such as the patient's overall life expectancy according to the patient's day and age. Database images are labeled into three categories, short-term, medium-term, and long-term, based on patient longevity after treatment. To improve the prediction results, different types of features were extracted and taught by different machine learning methods. The considered features include texture, volumetric, statistical, and deep features. The machine learning methods used include support vector machine, nearest neighbors, linear discriminant analysis, and decision tree.

The best prediction accuracy based on the classification was obtained using deep features extracted by a pre-trained convolutional neural network (CNN) and by linear discriminant analysis.

Deep learning approaches showed a good performance in the prediction of medical parameters such as survival rate time.

Extracting information from the heart sound signal and detecting the abnormal signal in the early stage can play a vital role in reducing the death rate caused by cardiovascular diseases. Therefore, many researches have been done in processing these signals up to now. So, this study aimed to determine the improvement of heart abnormalities diagnosis by extracting features from the heart sound signal by applying machine learning classification algorithms.

The present descriptive–analytical study was conducted at Kazerun Azad University in 2023. The research data were selected from the 2016 Physionet Challenge database. After pre-processing and noise removal, 6 new features and 35 features (41 features) used in previous researches were extracted from the heart sound signals. The 6 new features are " Relative Average Perturbation", " five-point Period Perturbation Quotient", "local shimmer (in dB)", " three-point Amplitude Perturbation Quotient " and " five-point Amplitude Perturbation Quotient " and " correlation of time center of signal and frequency center of signal". The extracted features were applied as input to four classifiers of random forest, support vector machine, K nearest neighbor and linear discriminant analysis. Accuracy, sensitivity and specificity of each classification were calculated. In order to investigate the impact of new features in the diagnosis of cardiac abnormalities, the results obtained were compared with studies that used similar data and classifications but extracted fewer features from the data. The collected data were analyzed using t-tests and logistic regression.

The highest accuracy and sensitivity were obtained in the Linear Discriminant Analysis classifier, which are 91.52 and 96.19, respectively. The highest specificity was obtained in the Random Forest classifier at the rate of 88.90. According to the obtained results, by adding new features, the three indices of accuracy, sensitivity and specificity are improved in the two classifiers of K-nearest neighbor and Linear Discriminant Analysis. Extraction of these features also increases the level of specificity in the Random Forest classification.

The results indicated that the extraction of new features led to increase in the accuracy, sensitivity and specificity in the diagnosis of cardiac abnormalities compared to the results of previous researches.

Diabetes is a chronic disease worldwide, with an increasing annual death rate. Many health professionals seek innovative ways to detect and treat it early. Rapid advances in machine learning have improved disease diagnosis. However, because of the small amount of labeled data, the frequency of null and missing values, and the imbalance of databases, creating an optimal predictor for disease diagnosis has become a great challenge. This study aimed to present a pipeline-based classification framework for predicting diabetes on two datasets of Indian diabetic patients with two classes (patient and healthy groups) and Iraqi with three classes (patient, healthy, and prediabetes groups).

An important part of this framework is preprocessing. Different ML models based on the One-Vs-One approach for the three-class mode are implemented in the framework. Because of the imbalance of the data set, besides the accuracy evaluation criterion, the area under the receiver operating characteristic (ROC) curve is also used. To increase the level of these two criteria, the Hyper-parameters of each model are optimized using optimization methods to build a powerful model with less training and testing time through various feature selection methods.

The proposed framework was assessed for diabetes prediction on two datasets of Indian and Iraqi diabetic patients. It was revealed that using AdaBoost for the Indian dataset (ACC=89.98, AUC=94.11) and random forest for the Iraqi dataset (ACC=98.66, AUC= 98.62), good accuracy and performance were obtained.

Regarding ACC parameters, precision, accuracy, recall, and F1-Score, the pipeline-based framework has an optimal performance in predicting diabetes, therefore, it can be used in clinical decision support systems.

Brain-computer interface systems provide the possibility of communicating with the outside world without using physiological mediators for people with physical disabilities through brain signals. A popular type of BCIs is the motor imagery-based systems and one of the most important parts in the design of these systems is the classification of brain signals into different motor imagery classes in order to transform them into control commands. In this paper, a new method of brain signal classifying based on deep learning methods is presented.

This cross-sectional study was conducted at Isfahan University of Medical Sciences, School of Advanced Technologies in Medicine, from February 2020 to June 2022. In the pre-processing block, segmentation of brain signals, selection of suitable channels and filtering by Butterworth filter have been done; then data has transformed to the time-frequency domain by three different kinds of mother wavelets including Cmor, Mexicanhat, and Cgaus. In the classification step, two types of convolutional neural networks (one-dimensional and two-dimensional) were applied whereas each one of them was utilized in two different architectures. Finally, the performance of the networks has been investigated by each one of these three types of input data.

Three channels were selected as the best ones for nine subjects. To separate 8-30 Hz, a 5th degree Butterworth filter was used. After finding the optimal parameters in the proposed networks, wavelet transform with Cgauss mother wavelet has the highest percentage in the both proposed architectures. Two-dimensional convolutional neural network has higher convergence speed, higher accuracy and more complexity of calculations. In terms of accuracy, precision, sensitivity and F1-score, two-dimensional convolutional neural network has performed better than one-dimensional convolutional neural network. The accuracy of 92.53%, which is obtained from the second architecture, as the best result, is reported.

The results obtained from the proposed network indicate that suitable, and well-designed deep learning networks can be utilized as an accurate tool for data classification in application of motion perception.

Background & Aims:

 Early diagnosis of liver diseases has a significant effect on the prevention of its complications as well as control and treatment of the disease. “Machine learning” is one of the branches of artificial intelligence that has many applications in the field of medical diagnosis. This study aimed to provide a model with high accuracy and reliability for diagnosing liver diseases using machine learning methods that can help physicians in the early diagnosis and control of liver diseases.

Materials & Methods:

 This applied-developmental study used the dataset of 583 liver patients. In order to more accurately diagnose of the people with liver diseases, the results of the three classifiers including: Random Forest, Support Vector Machine, and Artificial Neural Network were combined using Dempster-Shafer theory. Weka data mining tool and Python programming language were used to implement the model. The k-fold cross-validation method was applied to evaluate efficiency of the model.

The results showed that accuracy, sensitivity, and specificity in the proposed model were 91.47%, 89.52%, and 93.03%, respectively, which had a better performance than similar studies.

The proposed model in the studied statistical population has a better performance in diagnosing liver diseases, and can help physicians in early diagnosis of the disease and appropriate treatment of it in the early stages of it and thus prevent development of the disease.

The distribution of causes of death indicates the distribution of risk factors for death, and is a basis of planning and intervention to reduce risk factors. The quality of the registered information has problems due to the weakness of the processes of completing and issuing the death certificate or the coding method. The purpose of this study was to explain the challenges of death registration and to provide a solution in this regard.

This qualitative study was conducted in the second half of 2019 in Guilan University of Medical Sciences. The target population was the directors and experts of the death registration program. Sampling was done purposefully by counting. Data was collected through in-depth interviews using a questionnaire and simultaneous contractual content analysis to identify key themes. To ensure the validity and acceptability of the data, the participants and two research colleagues reviewed the data frequently.

According to the content analysis of 24 interviews, the main challenges of death registration included manpower, organizing the death registration system in the country, and death registration software system and its implementation. These themes were abstracted from 45 subcategories and 13 main categories.

Considering the challenges described by death registration managers and experts, the main proposed interventions to improve the death registration system include recruiting appropriate staff, empowering and motivating various human resources departments, developing internal and external cooperation, increasing public participation, monitoring and continuous assessment to identify the strengths and weaknesses of the death registration system and adressing them, attention to the development of death registration software and its required infrastructure such as Internet access and equipment, attention to the multiplicity of systems, and efforts to integrate them.

Glioma is the most common primary brain tumor in adults. Various machine learning tools via magnetic resonance imaging can make it a practical instrument in accurate and early diagnosis of tumors thereby assisting physicians in diverse diagnostic and therapeutic fields. The aim of this study is to automate the process of defining and determing the grade of glioma tumor with the use of a variety of learning algorithms.

This is a fundamental-applied study performed on multimodal MRI images of 285 patients with glioma tumors from the BraTS 2018 Challenge Database. In order to classify glioma tumors as high and low grade, first a was performed with U Net network for the definition purposes, then the results were incorporated for classification in VGG16 network to determine the exact grade of tumor.

The mean value of Dice Similarity Coefficient (DSC) for the classification designed for regions of the complete tumor, core of the tumor and the enhanced areas were 0.76, 0.70 and 0.71 respectively. The accuracy of the proposed classification based on VGG 16 network to determine the grade of tumor in both HGG and LGG groups was 99.01%.

Machine learning methods can he useful to determine the glioma tumor grade instead of using invasive proceedures like biopsy which in turn improves overall survival rate of these patients and their quality of life.

Background &

Mammography is one of the reliable methods for early detection of breast cancer. However, it is difficult for the radiologist to provide an accurate and uniform assessment of the massive mammograms produced in the extensive screening. Therefore, the presence of an intelligent system that is highly accurate in detecting the location of a cancerous mass will be very necessary and necessary. In this regard, this research, by using mammography images and image processing techniques, has been tried to get an accurate diagnosis of the location of breast cancer. For this purpose, first by using some digital image enhancement techniques, an attempt is made to increase the recognition of cancerous tissues, and then by using classification techniques, the precise separation of cancerous parts from healthy parts of the breast is done. In research, various techniques have been proposed to improve the detection of tumors in mammograms and the accuracy of breast cancer classification. The basic problems in breast mammography in identifying and classifying masses and microcalcifications are caused by various factors. One of these complications is due to the awkward and illogical shape of some clusters of calcifications. The boundaries of each of the microcalcifications in the cluster cannot be well defined, and the radiologist may not be able to make an accurate diagnostic decision about the clinical nature of the microcalcifications in an area, but he can usually identify suspicious areas. In the paper, they presented a CAD system for processing mammographic images. They used the compressed breast thickness parameter for feature selection showed the importance of breast compression and changes in breast composition and then applied it to a variety of mammography image processing tasks. Considering that breast thickness is a key parameter in calculations and is not usually recorded; they showed that breast thickness can be estimated from an image and examined its sensitivity on the estimates. Then they discussed how to simulate X-rays in each examination and also simulate the appearance of anatomical structures inside the breast. In the research, tissue characteristics were used to automatically evaluate breast tissue density in digital mammograms. In this approach, the target area is limited to breast tissue only; so that artifacts, background, and head muscles are removed.Breast cancer is the most common cancer among women and the second cause of cancer-related death in women. Mammography is a simple type of imaging and a tool for early detection of non-palpable breast cancers; however, examining and interpreting a large number of mammogram images is a challenging and time-consuming task, and the possibility of human errors is high. One of the most important deep learning methods is convolutional neural networks. In the article, the digital database for screening mammography from the CBIS version was used to improve data validation.

In this research, three types of architecture were designed in the two-class mode and one type of architecture was designed in the three-class mode. To design the network, the layers were arranged according to Figure 5, which uses an input layer of size 159 x 145 a two-dimensional convolution layer of size 20 x 8, and a maximum integration layer of size 5 x 2, and two fully connected layers. (The maximum integration layer was used because it uses the maximum amount of neuron clusters of the previous layer and also causes faster convergence, and improves generalization and selection of invariant features). The third designed network architecture is shown in such a way that one input layer three 2D convolutional layers three maximum integration layers and two fully connected layers are used, the size of each layer is shown in Table (3). Layering is equal to one. The training time is 6:37 and the accuracy obtained for the validation data is 92.58% and the test data is 86.5%.

The simulation results for 310 data for the second type of two-class architecture, the training time is 6:06, and the accuracy obtained for the validation data is 84.40% and 72.82% for the test data. Also, the simulation results for 1240 The data for the first type of two-class architecture, the training time is 3:44:54, and the accuracy obtained for the validation data is 51.72% and the test data is 51.69%.

After a series of pre-processing, the number of used images was selected as 310. Then two other types of architecture were designed, and by applying the processed data, the accuracy of the architectures for 310 data was 42.39%, 7and 2.82%, respectively. 79.34% was obtained. The accuracy of the architectures for 1240 data was 51.69%, 65.45%, 72.46%. In the three-class mode, 1318 images in the database were used, and due to the lack of the same size, the images were resized. Then the image mask was applied to the images and given to the designed convolutional neural network, and the data was classified into three classes. According to the pre-processing and operations that have been done, the accuracy of the network has increased (72.39%) and the result has improved. The advantage of the method is the increased accuracy of validation and test data.

The modern world today allows images to be received and stored digitally. To get better results, it is sometimes necessary to make changes to these images. These changes pursue three main goals: image processing, analysis, and comprehension. For this reason, computer image processing systems have been developed to perform these operations with better speed and accuracy. Four major processes occur in these systems: preprocessing, image quality enhancement, image conversion, and image classification and analysis. In these methods, using mathematics, rules have been created by the computer to simulate human visual elements, and it is an aspect of image analysis that is used for specific purposes. Skin imaging systems provide the ability to process images in high volume and with minimal time and cost, as well as increase the accuracy of diagnosis and classification of diseases. These systems, fatigue, human error and other weaknesses that the diagnostician can suffer. Do not have it (1). The first step in diagnosing skin diseases and analyzing digital images of patients with skin lesions is to take a color photograph of the lesion area. One of the most valid methods for this is the use of a dermoscopic device (2). Dermoscopy, also known as dermatoscopy, is an effective tool for dermatologists involved in early diagnosis. Using dermoscopically evaluated pigmented lesions, abnormal structural features are detected and the border of the lesions is accurately observed (3). Accordingly, benign lesions can be detected without the need for biopsy. Dermoscopy increases the accuracy of the diagnosis and helps GPs to correctly identify people with suspected lesions who need to be referred to a specialist. Dermoscopy is also effective in diagnosing non-pigmented skin lesions and inflammatory dermatoses. In dermoscopy, the skin is examined using a special microscope (4).

The proposed algorithm of this research can be divided into 7 separate steps (loading data set, data integration: data balancing with data amplification or data augmentation technique, data cleaning: clearing images to remove hair noise, slicing images to separate skin from skin Healthy, data conversion: data preparation, convolution neural network design (CNN) and training of the proposed model for image feature extraction, classification combination and mass learning by majority voting method). Which was implemented in Python language in Google Columbine environment and supervised. For this study, 25,331 dermoscopic images consisting of skin lesions were included (70% educational images, 15% experimental and 15% validation). In data preprocessing, the data were balanced, then the data cleaning operation was performed to remove hair noise, and the data reduction operation was performed to segment the images by separating the lesion from healthy skin. In the next process By designing the convolution neural network, training data were extracted for feature extraction, and by combining the classifiers, an automated system for diagnosing skin diseases was created and evaluated in dermoscopic images.

In the proposed method of hair noise removal, the quality of images is increased and also the separation of the lesion from healthy skin is optimally designed to accelerate image processing to extract high-level features in the convolutional neural network and increase the accuracy of diagnosis and classification to create An automated diagnostic system is a feature of this study compared to other studies. According to the research results, the use of an automated system for the diagnosis and classification of skin diseases and cancers for health-related care is recommended.

Today, the applications of artificial intelligence are not hidden from anyone. Among these, machine learning as one of the most important branches of this field has a special place in all sciences. Deep learning has proven its worth by using the basics of artificial neural networks in solving many issues in the field of medical image processing such as classification. Experts also based on various experiences of using training methods to conclude that there is no single specific training algorithm that can be successful for all applications and has the highest accuracy. Hence they suggest combined learning. According to the important results, although each of the algorithms had a successful performance individually, but combining several algorithms with each other has led to higher accuracy and less error-making decisions. This study is a step towards helping physicians and specialists in diagnosing skin diseases and benign and malignant skin cancers and can help GPs or other physicians to better manage high-risk lesions. Secondary triage as well as avoid unnecessary treatments and minimize biopsy, which is an invasive and costly procedure. This research helps to provide health-related care, forecasting and treatment, as well as cost savings for both patients and health care providers. Also, in deprived areas and far from the specialist, dermoscopic devices with the help of this algorithm can cause timely treatment and reduce patients' costs and time in the field of diagnosis and, if necessary, referral of patients to the desired specialist. Available as a commercial software package. This software package has the ability to connect to dermoscopic devices. By connecting this software package to dermoscopy, a device is created to quickly diagnose skin diseases and cancers. The greatest value of this dissertation is that it is used as a benchmark for designing future studies and evaluating skin cancer diagnosis techniques in patients who are usually examined by a general practitioner and specialist. The findings of the present study are also consistent with the results of Andre and Pachko (2019) research on the diagnosis of skin cancer based on deep learning and entropy for Perth samples (6).

Background and Purpose:

Gastrointestinal poisoning is a common type of poisoning caused by ingested foods, medicines and toxic substances. Considering the valuable sources of Persian medicine, this research has focused on classification, diagnosis and treatment of medical emergencies in gastrointestinal poisoning during the 3rd to 7th centuries AH in Persian Islamic medical texts in comparison with modern medicine.
 

This is a review study, in which relevant Farsi, Arabic and English keywords were searched in in the books Firdaws al-Hikma fi al-Tibb, Al-Qanun fi al-Tibb, Al-Hawi fi al-Tibb and Zakhireye Khwarazmshahi. Retrieved contents were taken notes of, filed and then studied and analyzed. Finally, the extracted results were summarized using a two-person team with traditional medicine and the history of medical sciences specialties, and results were reported.

The main results of this research indicate that traditional medicine physicians categorized gastrointestinal poisoning based on temperament, type of toxic substance (plant, animal and mineral), lethality and the essence of the toxic substance. Diagnosis methods included examination and close investigation of patients, body odor, examination of vomited substance and using pets and birds. In treatment of poisoning, general methods like taking quick actions, vomiting, drinking milk, enema or injection of fluid into the lower bowels; and specific treatments based on the type of toxic substance were used. Pharmacological and non-pharmacological measures were considered.

The findings of the present research show that the introduced patterns in the historical medical texts of Iran in classification, diagnosis and treatment of gastrointestinal poisoning have a lot in common with the modern views and approaches. Therefore, it is possible to achieve newer and easier methods with fewer side effects in treatment of gastrointestinal poisoning and promotion of present methods by reviewing traditional texts and researches and conducting clinical trials on properties of traditional medicines and contents.

Evaluation of the electroencephalogram of schizophrenic patients has been the subject of many recent studies. However, accurate diagnosis of schizophrenia using the electroencephalogram is still a challenging issue.

This paper was aimed to investigate the information discrepancy between one brain channel and other electrodes in two groups of schizophrenic patients and healthy individuals. Furthermore, the capability of the extracted features in the problem of classification of the two groups was investigated.

In the present analytic observational study, 19 channels of the electroencephalogram of 14 patients with schizophrenia (7 males with an average age of 27.9 ± 3.3 years and 7 females with a mean age of 28.3 ± 4.1 years), who were hospitalized at the Institute of Psychiatry and Neurologists in Warsaw, Poland, were used. In addition, data from 14 healthy individuals (7 males and 7 females with an average age of 26.8 ± 2.9 and 28.7 ± 3.4 years, respectively) were analyzed as a control group. Cross information potential and Cauchy-Schwartz mutual information between each electroencephalogram electrode and all the other electrodes were calculated. Using two strategies, the performance of the support vector machine was evaluated: (1) the mutual information of an electroencephalogram channel with other channels, and (2) the combination of the mutual information of all brain channels.

The results showed that using mutual information between electroencephalogram channels, the diagnosis accuracy increases up to 100%. For both indices, the mutual information between O2 and the other channels provided the highest classification performance.

These results nominated the proposed system as a superior one compared to the state-of-the-art electroencephalogram schizophrenia diagnosis tools.

In a pre-hospital emergency, identifying high-risk medical patients and appropriate decision making is very important. The classifying of life-threatening risks in pre-hospital settings can improve the decision-making process. This study was purposed to classify the risk level of death in patients in pre-hospital emergency settings.

This study was a descriptive longitudinal study design that performed on the patients requesting the pre-hospital emergency services. This study was a descriptive longitudinal descriptive study that lasted from May 1 to the end of August 2017. 675 patients participated in this study. A demographic questionnaire and pre-hospital internal emergency alert scale were used for data collection. Then the patients' condition was followed up until discharge from the emergency department.

The mean score of the Pre-Hospital Internal Emergency Warning Scale was 11.95 ± 6.21. The odds ratio for each Prehospital Medical Early Warning Scale (Pre-MEWS) score was 1.193, which indicated for each unit increase in Pre-MEWS scores, the risk of death of patients increased by 19.3%. Then, the patients classified into three levels of green, yellow, and red based on Pre-MEWS scores. The Area Under the Rock curve for Pre-MEWS scores to identify death and the need for hospitalization in intensive care units was equal to 0.847 (CI=0.793-0.901), and0.755 (CI= 0.711-0.799) respectively).

The Pre-MEWS scale can use for classifying and triage of patients in pre-hospital emergencies, especially in times of disaster. According to the Pre-MEWS scale, patients who are in the third level or green color can transfer to the hospital with more delay, and this can improve the quality of pre-hospital emergency services in times of disaster.

In recent years, some studies have examined the gait patterns of neurodegenerative diseases utilizing signal processing techniques and machine learning algorithms. The aim of this study was to provide an automated system for distinguishing Huntington's disease, amyotrophic lateral sclerosis (ALS), and Parkinson's disease from healthy control group using dynamic analysis of gait pattern (more precisely, stride time). In addition, we examined the effect of fusion of features obtained from the left and right feet.

First, polar-based measures were extracted from lagged Poincaré maps. The optimal latency of the map was estimated using the mutual information algorithm. Then, five feature-level fusion strategies were presented. The classification was performed using the feed-forward neural network; while the effect of changing the network parameter was also investigated. The proposed system was evaluated using the data available in the Physionet database, which includes 16 records of the control group (14 females and 2 males; 20-74 years), 20 records of Huntington's disease (14 females and 6 males; 29-71 years), 13 records of ALS (3 women and 10 men; 36-70 years) and 15 records of Parkinson's disease (5 women and 10 men; 44-80 years).

Using the fourth fusion strategy, the maximum accuracy of 93.47% was obtained in separating the control and Huntington groups. Applying the second fusion algorithm, the control/Huntington and control/Parkinson groups were separated with the accuracy rate of 92.92% and 91.93%, respectively. The highest accuracy of the first fusion algorithm was 91.72% in classifying the control group and ALS. The third fusion algorithm was able to provide a 91.13% classification accuracy in separating the control and Huntington groups. The performance of the algorithm in separating patient groups was weaker.

The proposed system performed well compared to previously published algorithms. Further studies on intelligent classification algorithms and the development of the suggested method could pave the way for preclinical diagnosis of neurodegenerative diseases.

Given that a huge amount of cost is imposed on public and private hospitals from the department of liver diseases, it is necessary to provide a method to predict liver diseases. This study aimed to propose a hybrid model based on the Ant Lion Optimization algorithm and K-Nearest Neighbors algorithm to diagnose liver diseases.

This descriptive-analytic study proposed a hybrid model based on machine learning algorithms to classify individuals into two categories, including healthy and unhealthy (those with liver diseases). The proposed model has been simulated using MATLAB software. The datasets used in this study were obtained from the Indian Liver Patient Dataset available in the Machine Learning Repository at the University of Irvine, California. This dataset contains 583 independent records, including 10 features for liver diseases

After pre-processing, the dataset was randomly divided into 20 categories of the entire dataset, which included different training and test data. In each category of the dataset, 90% and 10% of the data were used for training and test, respectively. Regarding all features, the results obtained the most accurate mode at 95.23%. Moreover, according to the criteria of specificity and sensitivity accuracy, the corresponding values were 93.95% and 94.11%, respectively. Furthermore, the accuracy of the proposed model along with five features was estimated at 98.63%.

This model was proposed to diagnose and classify liver diseases along with an accuracy rate of higher than 90%. Healthcare centers and physicians can utilize the results of this study.