Evaluating Diabetes Detection Methods: A Multilinear Regression Approach vs. Other Machine Learning Classifiers

Abstract

Machine learning has become an important tool in many fields, including healthcare. In this research paper, we aim to implement diabetes dataset in multi-linear regression and compare its performance with different classifiers of machine learning. The novelty of this research lies in the evaluation of the diabetes dataset using multilinear regression and subsequent comparison of its performance against several other classifiers, including Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machines (SVM). This comparative analysis aims to assess and benchmark their respective performances.  etc. Our results show that multi-linear regression achieved an accuracy of 80.5%,  However, other classifiers such as random forest, and logistic regression outperformed linear regression, achieving accuracy scores of 81.4% and  81.25%, respectively. Furthermore, we observed that decision tree, KNN, and SVM, which are often used for classification tasks, did not perform well on this dataset, achieving an accuracy of only 78.7%, 80.5%, and 79.6% respectively. This suggests that the model's performance can be greatly impacted by the classifier selection. Our findings suggest that linear regression can be used for predicting diabetes, other classifiers such as random forest, and logistic regression are more effective for this dataset. To choose the best classifier for a given job, it is crucial to assess and contrast the performance of several classifiers.