Comparative analysis of predictive machine learning algorithms for diabetes mellitus

Abstract

Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.

Performance evaluation of feature selections on some ML approaches for diagnosing the narcissistic personality disorder

“…We develop the model using WEKA (version 3.9.2) in this research. The WEKA platform simplifies the construction of several data  ISSN: 2302-9285 analysis techniques and offers a JAVA programming language API [37]. It provides tools for categorizing, regressing, grouping, eliminating superfluous traits, creating association rules, and displaying the dataset.…”

A computational intelligent analysis of autism spectrum disorder using machine learning techniques

“…The decision tree has a hierarchical tree structure with a root node, branches, internal nodes, and leaf nodes [25]. It also uses both classification and regression methods.…”

“…Machine learning (ML) techniques have been utilized to yield favourable results, and future research efforts may involve the application of deep learning and multi-model fusion methods to further improve accuracy. The class/ASD dataset [24], [25] has been employed, with subgroups based on age and verbal intelligence quotient (VIQ) used for analysis via the RF model. Although the obtained classification accuracy is relatively low, it can serve as a useful reference for clinical diagnosis and early detection of ASD [26].…”

Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation

“…Other work that employs machine learning techniques for the detection of diabetes was conducted by Kangra and Singh [ 27 ], where various machine learning algorithms were compared to identify the most efficient in predicting diabetes. The algorithms analyzed were support vector machine, naive Bayes, k-nearest neighbor, random forest, logistic regression, and decision tree.…”