Improving the Accuracy of Diabetes Diagnosis Applications through a Hybrid Feature Selection Algorithm

• Published in: Neural processing letters Vol. 55; no. 1; pp. 153 - 169
• Main Authors: Li, Xiaohua, Zhang, Jusheng, Safara, Fatemeh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Classification; Complex Systems; Computational Intelligence; Computer Science; Condition monitoring; Coronaviruses; COVID-19; Data mining; Datasets; Decision trees; Diabetes; Diagnosis; Disease; Feature selection; Genetic algorithms; Heart diseases; Hypertension; Insulin; Machine learning; Medical personnel; Methods; Neural networks; Optimization algorithms; Optimization techniques; Particle swarm optimization; Patients; Search algorithms; Support vector machines; K-means; Diabetes diagnosis application; Genetic algorithm; Coronavirus disease pandemic; Particle swarm optimization; Artificial intelligence; Harmony search algorithm
• ISSN: 1370-4621; 1573-773X; 1573-773X
• DOI: 10.1007/s11063-021-10491-0

Artificial intelligence is a future and valuable tool for early disease recognition and support in patient condition monitoring. It can increase the reliability of the cure and decision making by developing useful systems and algorithms. Healthcare workers, especially nurses and physicians, are overworked due to a massive and unexpected increase in the number of patients during the coronavirus pandemic. In such situations, artificial intelligence techniques could be used to diagnose a patient with life-threatening illnesses. In particular, diseases that increase the risk of hospitalization and death in coronavirus patients, such as high blood pressure, heart disease and diabetes, should be diagnosed at an early stage. This article focuses on diagnosing a diabetic patient through data mining techniques. If we are able to diagnose diabetes in the early stages of the disease, we can force patients to stay home and care for their health, so the risk of being infected with the coronavirus would be reduced. The proposed method has three steps: preprocessing, feature selection and classification. Several combinations of Harmony search algorithm, genetic algorithm, and particle swarm optimization algorithm are examined with K-means for feature selection. The combinations have not examined before for diabetes diagnosis applications. K-nearest neighbor is used for classification of the diabetes dataset. Sensitivity, specificity, and accuracy have been measured to evaluate the results. The results achieved indicate that the proposed method with an accuracy of 91.65% outperformed the results of the earlier methods examined in this article.