A Proposed E-SVM Framework for Early Diagnosis of Type 2 Diabetes Mellitus Prediction
Keywords:
Machine Learning, Kaggle, Positive, Negative, ESVM.Abstract
Diabetes is a widely observed metabolic condition distinguished by heightened amounts of glucose in the bloodstream. The identification of this illness in its early stages presents difficulties owing to its intricate reliance on multiple elements. Type 2 diabetes (T2D) is a chronic metabolic disorder that has a substantial impact on a considerable proportion of the global population. The development of crucial decision support systems is necessary in order to provide assistance to medical practitioners during the diagnostic process. The application of many machine learning (ML) algorithms in the prediction of this particular disease has attracted considerable attention, particularly due to its potential for early identification and effective intervention. The present study aims to create a predictive model with the objective of attaining a notable level of classification accuracy in the context of type 2 diabetes. The present study utilizes the Enhanced Support Vector Machine (ESVM) algorithm to predict and screen for diabetes. The dataset used in this study was obtained from Kaggle and consisted of 768 patients, both with and without diabetes, belonging to the Pima Indian population. The dataset under consideration consists of data from 768 patients, encompassing eight primary attributes and a goal column indicating the outcome as either "Positive" or "Negative." The experiment was conducted using the Python programming language, and the results of the demonstration indicate that the utilization of a ML model yields enhanced efficiency in predicting diabetes.
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