Detection and prediction of diabetes using effective biomarkers

Abstract

Diabetes is a prevalent and costly condition, with early diagnosis pivotal in mitigating ‎its progression and complications. The diagnostic process often contends with data ‎ambiguity and decision uncertainty, adding complexity to achieving definitive ‎outcomes. This study addresses the diabetes diagnostic challenge through data mining ‎and machine learning techniques. It involves training various machine learning ‎algorithms and conducting statistical analysis on a dataset comprising 520 patients, ‎encompassing both normal and diabetic cases, to discern influential features.‎ Incorporating 17 features as classifier inputs, this research evaluates the diagnostic ‎performance using four reputable techniques: support vector machine (SVM), random ‎forest (RF), multi-layer perceptron (MLP), and k-nearest neighbor (kNN). The outcomes ‎underscore the SVM model's superior performance, boasting accuracy, specificity, and ‎sensitivity values of 98.78±1.96%, 99.28±1.63%, and 97.32±2.45%, ‎respectively, across 50 iterations. The findings establish SVM as the preferred method ‎for diabetes diagnosis.‎ This study highlights the efficacy of data mining and machine learning models in ‎diabetes diagnosis. While these methods exhibit respectable predictive accuracy, their ‎integration with a physician's assessment promises even better patient outcomes.‎

Keywords

• Data mining
• diabetes
• SVM
• detection
• prediction