Diabetes disease prediction system using HNB classifier based on discretization method

Abstract

Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way – through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.

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“…With the recent advancement of high-throughput technology and the overwhelming amount of omics data, data collection has increased considerably, thus shifting the perspective It has proven successful in diabetes disease prediction, optical character recognition, face identification, and others [12], [13], [14], [15]. ML is a set of algorithms to improve prediction accuracy by learning and analyzing the patterns from large experimental datasets.…”