A Novel Memetic Feature Selection Algorithm

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In feature selection algorithms search strategies are key aspects. Since feature selection is an NP-Hard problem; therefore heuristic algorithms have been studied to solve this problem. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for a classification problem. It incorporates a filter method in the genetic algorithm to improve classification performance and accelerates the search in identifying core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the multivariate feature information. Empirical study on commonly data sets of the university of California, Irvine shows that the proposed method outperforms existing methods

Comparison of the Decision Tree Models to Intelligent Diagnosis of Liver Disease

Background: Liver is one of the vital organs of human body and its health is of utmost importance for our survival. Automatic classification instruments, as a diagnostic tool, help to reduce the working load of doctors. But the concern is that, liver diseases are not easily diagnosed and there are many causes and factors related to them. The purpose of this research is to compare the decision tree models to intelligent diagnosis of liver disease. Intelligent diagnosis models used in this research are QUEST, C5.0, CRT and CHAID. Material and Methods: Data were collected from the records of 583 patients in the North East of Andhra Pradesh, India. Four tree models were compared by the specificity, sensitivity, accuracy, and area under ROC curve. Results: The accuracy of the classification tree models; QUEST, C5.0, CRT, and CHAID were 73%, 71%, 75%, and 86% respectively. Conclusion: CHAID model was considered as the best model with the highest precision. Therefore; CHAID model can be proposed in the diagnosis of the liver disease. This paper is invaluable in terms of research activities in the field of health and it is especially important in the allocation of health resources for risky people

Can Breast Cancer Survival be predicted by Risk Factors? Machine Learning Models

Breast cancer is a kind of cancer with high mortality among women. With early diagnosis of breast cancer (up to five years after cell division) survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for early diagnosis of benign or malignant tumors. Automatic classification systems as a diagnostic tool can reduce the workload of doctors. Intelligent methods to predict Breast cancer survival which are used in this study consist of Naïve Bayes, Trees Random Forest, 1NN, AdaBoost, SVM, RBF Network and Multilayer Perceptron. In this study 900 patient records are used. These records have been registered at Cancer Registry Organization of Kerman Province, in Iran. For evaluate the proposed models, K-fold cross validation is used. Seven models of machine learning are compared base on specificity, sensitivity and accuracy. The accuracy of the seven models are .95%, .96%, .91%, .94%, .94%, .95% and .95% respectively. Our result showed that trees Random Forest model was the best model with the highest level of accuracy. Therefore, Trees Random Forest model is recommended to Breast cancer survival

Memetic Feature Selection Algorithm Based on Efficient Filter Local Search

Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. In this paper, we have proposed a method based on memetic algorithm to find an efficient feature subset for classification purposes. It incorporates a filter method in wrapper method to improve classification performance and accelerates the search in identifying core feature subsets. Especially, this method deletes or adds a feature from a feature subset based on the multivariate feature information. Empirical study on commonly data sets of the University of California, Irvine shows that the proposed method outperforms existing methods. Furthermore, we have investigated several major issues of memetic algorithm to identify a good balance between local search and genetic search so as to maximize search quality in the hybrid filter and wrapper memetic algorithm

Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review

Abstract Background and Aim Schizophrenia and bipolar disorder (BD) are critical and high‐risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full‐text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion ML can precisely predict results and assist in making clinical decisions‐concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research