amin golabpour

The diagnosis of NAFLD typically involves the use of the FibroScan test, which can be costly. More affordable options, like liver enzyme and hematology tests, cannot diagnose fatty liver disease; they only serve as preliminary tools for its diagnosis.

In this study, a machine-learning model was developed to diagnose fatty liver disease using demographic information, liver enzymes, and hematology tests. Data was extracted from the records of 1078 patients who visited Haj Marafi Hospital between 2018 and 2023, encompassing 25 dependent variables. After preprocessing, the data was reduced to 531 records. A multi-objective particle swarm optimization algorithm was used to impute missing data. Following preprocessing, a support vector machine (SVM) algorithm was applied to the data, and the performance of the proposed algorithm was compared and evaluated against similar algorithms.

During preprocessing, records with more than 20% missing data were removed, and the remaining data were imputed. The data was then divided into training and testing sets (70-30 split). The radial basis function (RBF) SVM was applied to the training data, resulting in sensitivity, specificity, and accuracy of 96.24%, 90.86%, and 93.55%, respectively. For the test data, these rates were 80%, 77.22%, and 78.62%.

This study demonstrated that machine learning algorithms can diagnose NAFLD more cost-effectively.

Building clinical decision support models to automatically extract knowledge from data helps physicians in early diagnosis of disease. Interpretability of the diagnostic rules of these models for understanding how they make decisions and increasing confidence in their output is a key indicator in determining their efficacy.

In this retrospective study, an automated hybrid rule extraction model is proposed for type 2 diabetes. In order to evaluate the model, the PIMA Diabetes dataset including 768 records and 9 variables was used. After removing the missing and outlier data in the data preprocessing stage, a proposed fuzzy-genetic hybrid model was implemented using MATLAB software to extract the rules. A self-organizing chromosomal structure was used to eliminate the complexity of setting genetic algorithm operators and facilitate the re-implementation of the model in other applications.

The accuracy of the proposed model on the PIMA dataset was 79.05%. This accuracy was obtained by two fuzzy rules, each of which contained only two independent variables. In addition, two single diagnostic rules for diabetic and non-diabetic individuals were presented with accuracy of 70.83% and 81.48%, respectively. The number of pregnancies, body mass index, diastolic blood pressure, diabetes pedigree function, plasma glucose concentration, and triceps skinfold thickness were the most effective factors in having or not having diabetes in the extracted rules.

The proposed model with high accuracy and interpretability is quite suitable in producing an accurate and highly interpretable set of rules as well as single rules for diagnosing diabetes or absence of diabetes. Due to its self-organizing ability, it can also be used for other binary classification purposes.

Artificial intelligence has been changingthe way healthcare has been provided in many high-risk environments or areas with poor healthcare facilities. The emergence of epidemics and new diseases, as well as the crucial role of early diagnosis in prevention and the adoption of more effective treatments have highlighted the need for accurate design and self-organization of Clinical Decision Support Systems (CDSSs).

In this study, a CDSS based on a neural networks (NN) and genetic algorithm is proposed. Since, on the one hand,the performance of the neural network (NN) is highly dependent on its parameters, and on the other hand, there is a constant need for optimization experts to fine-tune the parameters in the use of new data, a new chromosomal structure is proposed to automatically extract the optimal NN architecture, the number of layers and neurons. The goal is to increase the reusability of the model and ease of use by health experts.

To evaluate the performance of the model, two standard breast cancer (BC) datasets, WBC and WDBC, were used. The model uses 70% of the data set for training and the remaining equally used for evaluation and testing. The test accuracy of the proposed model on WBC and WDBC datasets was 98.51 and 97.55%, respectively. The optimal NN architecture on WBC consisted a three-hidden layers with 18, 15 and 19 neurons in each layers, and on WDBC consisted one hidden layer with a single neuron.

The proposed chromosomal structure is able to derive optimal NN architecture. In according to the high classification accuracy of the model in the diagnosis of BC and providing the different architectures in accordance with the hardware implementation considerations, the proposed model can be used effectively in the diagnosis of other diseases.

Diagnosis of high - risk maternal pregnancy is one of the most important issues during pregnancy and can be of great help to pregnant mothers. Also, early diagnosis can reduce mortality and morbidity in mothers.

In this study, the data of 1014 pregnant mothers were used, which includes 272 people with high - risk pregnancies, 742 people with medium - risk and low - risk pregnancies. Also, the data include six independent variables. A combi nation of Bayesian belief network algorithms and particle optimization was used to predict pregnancy risk.

For validation, the data model was divided into two sets of training and testing based on the method of 30 - 70. Then the proposed model was d esigned by training data. Then the model for training and testing data was evaluated in terms of accuracy parameters 99.18 and 98.32% accuracy were obtained, respectively. It has also performed between 0.5 and 8% better than similar work in the past.

 In this study, a new model for designing Bayesian belief network was presented and it was found that this model can be useful for predicting maternal pregnancy risk.

Improvement of technology can increase the use of machine learning algorithms in predicting diseases. Early diagnosis of the disease can reduce mortality and morbidity at the community level.

In this paper, a clinical decision support system for the diagnosis of gestational diabetes is pres ented by combining artificial neural network and meta - heuristic algorithm. In this study, four meta - innovative algorithms of genetics, ant colony, particle Swarm optimization and cuckoo search were selected to be combined with artificial neural network. Th en these four algorithms were compared with each other. The data set contains 768 records and 8 dependent variables. This data set has 200 missing records, so the number of study records was reduced to 568 records.

The data were divided into two sets of training and testing by 10 - Fold method. Then, all four algorithms of neural - genetic network, ant - neural colony network, neural network - particle Swarm optimization and neural network - cuckoo search on the data The trainings were performed and then ev aluated by the test set. And the accuracy of 95.02 was obtained. Also, the final output of the algorithm was examined with two similar tasks and it was shown that the proposed model worked better.

In this study showed that the combination of t wo neural network and genetic algorithms can provide a suitable predictive model for disease diagnosis.

Extracting effective rules from medical data with two indicators of accuracy and high interpretability is essential to increase the accuracy and speed of diagnosis by specialists. As a result, the production of medical assistant systems that are able to detect the rules governing the data plays a vital role in early detection of the disease and thus increase the chances of treatment, disease control and maintaining the quality of life of patients.

In this paper, a system of automatic extraction of rules from medical data by a new hybrid method based on fuzzy logic and genetic algorithm is presented. Genetic algorithms are used to automatically generate these rules. The Parkinson UCI dataset including 195 records and 23 variables was used to evaluate the proposed method based on the criteria of interpretability, accuracy, sensitivity and specificity.

The evaluation of the proposed model on the Parkinson's dataset was the accuracy of 84.62%. This accuracy is supported by 4 fuzzy rules with an average rule length of 2 and using 7 linguistic terms extremely low, very low, low, normal, high, very high and extremely high. All fuzzy membership functions that represent each term have the same width.

The proposed method, based on the three criteria of low number of rules, short rule length and symmetric membership functions with equal width for all variables, is quite suitable for automatic production of accurate and compact rules with high interpretability in medical data. . A 90% dimensionality reduction in the experimental evaluation showed that this model could be used to implement real-time systems.

With the growing rate of cesarean sections, rising morbidity and mortality thereafter is an important health issue. Predictive models can identify individuals with a higher probability of cesarean section, and help them make better decisions. This study aimed to investigate the biopsychosocial factors associated with the method of childbirth and designed a predictive model using the decision tree C4.5 algorithm. In this cohort study, the sample included 170 pregnant women in the third trimester of pregnancy referring to Shahroud Health Care Centers (Semnan, Iran), from 2018 to 2019. Blood samples were taken from mothers to measure the estrogen hormone at baseline. Birth information was recorded at the follow-up time per 30-42 days postpartum. Chi square, independent samples t test, and Mann-Whitney were used for comparisons between the two groups. Modeling was performed with the help of MATLAB software and C4.5 decision tree algorithm using input variables and target variable (childbirth method). The data were divided into training and testing datasets using the 70-30% method. In both stages, sensitivity, specificity, and accuracy were evaluated by the decision tree algorithm. Previous method of childbirth, maternal body mass index at childbirth, maternal age, and estrogen were the most significant factors predicting the childbirth method. The decision tree model’s sensitivity, specificity, and accuracy were 85.48%, 94.34%, and 89.57% in the training stage, and 82.35%, 83.87%, and 83.33% in the testing stage, respectively. The decision tree model was designed with high accuracy successfully predicted the method of childbirth. By recognizing the contributing factors, policymakers can take preventive action. It should be noted that this article was published in preprint form on the website of research square (https://www.researchsquare.com/article/rs-34770/v1).

Heart disease is known as one of the most important causes of death in today's society and sofar no definitive method has been found to predict it and several factors are effective in contracting this disease. Therefore, the aim of this study was to provide a data mining model for predicting heart disease.

This study used standard data from UCI. These data include four Cleveland, Hungarian, Swiss and Long Beach VA databases. These data include 13 independent variables and one dependent variable. The data are missing, and the EM algorithm was used to control this loss, and atthe end of the data, a suggestion algorithm was implemented that combined the two random forest algorithms and the artificial neural network.

In this study, data was divided into two training sets and 10-Fold method was used. To evaluate the algorithms, three indicators of sensitivity, specificity, accuracy were used and the accuracy of the prediction algorithm for four data Cleveland, Hungarian, Switzerland and Long Beach VA reached 87.65%, 94.37%, 93.45% and 85%, respectively. Then, the proposed algorithm was compared with similar articles in this field, and it was found that this algorithm is more accurate than similar methods.

The results of this study showed that by combining the two algorithms of random forest and artificial neural network, a suitable model for predicting heart attacks can be provided.

Breast cancer is one of the most common types of cancer and the most common type of malignancy in women, which has been growing in recent years. Patients with this disease have a chance of recurrence. Many factors reduce or increase this probability. Data mining is one of the methods used to detect or anticipate cancers, and one of its most common uses is to predict the recurrence of breast cancer.

Out of 699 patients with breast cancer, 458 (66%) of them did not relapse and 241 (34%) of their cancer recurred. This information was collected from 91 to 94 years of history of patients with breast cancer in the academic Jihad. In this study, the combination of two nearest neighboring algorithms and a genetic algorithm are proposed to predict the relapse of patients with breast cancer. First, the nearest neighboring algorithm is presented to predict the recurrence of breast cancer. Then, using the genetic algorithm, the dependent variables are reduced to make the model more appropriate.

The number of dependent variables is 14 variables, which is reduced by 6 genetic algorithms to better predict the model. To evaluate the model, the health parameter is used, which is 77.14% for the proposed model, which could not be more suitable for other methods.

In this study, the proposed algorithm was examined with other predictive methods and it was determined that the proposed algorithm is better.

Diabetes is a global health challenge that cusses high incidence of major social and economic consequences. As such, early prevention or identification of those people at risk is crucial for reducing the problems caused by it. The aim of study was to extract the rules for diabetes diagnosing using genetic programming.

This study utilized the PIMA dataset of the university of California, Irvine. This dataset consists of the information of 768 Pima heritage women, including 500 healthy persons and 268 persons with diabetes. Regarding the missing values and outliers in this dataset, the K-nearest neighbor and k-means methods are applied respectively. Moreover, a genetic programming model (GP) was conducted to diagnose diabetes as well as to determine the most important factors affecting it. Accuracy, sensitivity and specificity of the proposed model on the PIMA dataset were obtained as 79.32, 58.96 and 90.74%, respectively.

The experimental results of our model on PIMA revealed that age, PG concentration, BMI, Tri Fold thick and Serum Ins were effective in diabetes mellitus and increased risk of diabetes. In addition, the good performance of the model coupled with the simplicity and comprehensiveness of the extracted rules is also shown by the experimental results.

GPs can effectively implement the rules for diagnosing diabetes. Both BMI and PG concentration are also the most important factors to increase the risk of suffering from diabetes.

Due to the thalassemia severe complications, prediction of mortality or patients survival has a great importance in early treatment phases. This study purpose was to predict the mortality rate of patients with thalassemia major and thalassemia intermedia, by the use of the binary logistic regression algorithm and genetic algorithm combination.

This retrospective cohort study was conducted on 909 thalassemia patients by using a questionnaire during 2004-2014. The data of all patients referring to Imam Reza Hospital from 2004 to 2014 have been considered. This study predictive variable is considered to be death or survival of the patient. In this research, we embedded the missing data by the use of the proposed data mining model and MICE algorithm. Totally, 100 patients were excluded from this research, due to the missing or out-of-range data. Death was considered as dependent variable. Also, a predictive model was designed in order to
predict the patient mortality using MATLAB language.

Mean age of the thalassemia patients was 25.7±9.04 years old and at the end of the study death was reported in 185 subjects. Additionally, there were also 26 independent variables. Moreover, the missing variables mean for each patient was 1.8±0.81. The combined predictive model was able to predict the patient survival rate with 94.35% accuracy. In this research, it was found out that 26 independent variables, which were collected from 12 variables were patient mortality predictors. Also, missing data imputation is an important method for increasing the data mining algorithms efficiency.

According to this study results, the use of missing algorithm with the data analysis aid yielded more accurate results, in comparison with the MICE algorithm. Furthermore, 12 parameters affected the patient mortality prediction, which were extracted by the genetic algorithm. Accuracy of the predictive model for the patient death detection was favorable. Consequently, it is recommended to use this model in order to predict the patient mortality.

Adult T-cell leukemia/lymphoma (ATLL) is caused by human T-cell lymphotropic virus type-1 (HTLV-1). HTLV-1 oncogenes can induce malignancy through controlled gene expression of cell cycle checkpoints in the host cell. HTLV-I genes play a pivotal role in overriding cell cycle checkpoints and deregulate cellular division. In this study, we aimed to determine and compare the HTLV-1 proviral load and the gene expression levels of cyclin-dependent kinase-2 (CDK2), CDK4, p53, and retinoblastoma (Rb) in ATLL and carriers groups. A total of twenty-five ATLL patients (12 females and 13 males) and 21 asymptomatic carriers (10 females and 11 males) were included in this study. TaqMan real-time polymerase chain reaction assay was used for evaluation of proviral load and gene expression levels of CDK2, CDK4, p53, and Rb. Statistical analysis was used to compare proviral load and gene expression levels between two groups, using SPSS version 18. The mean scores of the HTLV-1 proviral load in the ATLL patients and healthy carriers were 13067.20±6400.41 and 345.79±78.80 copies/104 cells, respectively (P=0.000). There was a significant correlation between the gene expression levels of CDK2 and CDK4 (P=0.01) in the ATLL group. Our findings demonstrated a significant difference between the ATLL patients and healthy carriers regarding the rate of proviral load and the gene expression levels of p53 and CDK4; accordingly, proviral load and expression levels of these genes may be useful in the assessment of disease progression and prediction of HTLV-1 infection outcomes.