Effectiveness of Machine Learning & Deep Learning Models for Diabetes Prediction

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Effectiveness of Machine Learning & Deep Learning Models for Diabetes Prediction

Priyabrata Sahu , Jibendu Kumar Mantri

DOI : 10.46335/IJIES.2023.8.3.5

Abstract: Hyperglycemia alters blood sugar levels. Hyperglycemia, often known as high blood sugar, is the result of uncontrolled diabetes, which may cause nerve and blood vessel problems. Hyper-glycemia, or high blood sugar, is a typical result of insufficient glucose management and is associated with several significant health complications, most notably those affecting the nerves and blood vessels. Machine learning (ML) and deep learning (DL) predictive models have seen tremendous development throughout industries, including health care, making early diagnosis of diabetes a breeze. The treatment of chronic diabetes, one of the world's most prevalent illnesses, might benefit greatly from improved diagnostic efficiency. Here, we examine the relative merits among several ML and DL approaches to the problem of early diabetic illness prediction. The primary objective of this research study is to organize and conduct out diabetes diagnosis and prognosis with several machine learning approaches and then analyze the results of these methods to determine which one is the most accurate classifier. In this work, we take a multifaceted approach to diabetes and its prediction by investigating a wide range of disease-related characteristics. Many Machine Learning classification methods, including Random Forest (RF), Logistic regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Decision Tree (DT), Gradient Boosting, are applied to the canonical Pima Indian Diabetes Dataset (PIDD) (GB). There is a wide range of precision amongst the models used here. A technology that can accurately predict diabetes is shown in this research. The results of this research indicate that one of the Data mining models, random forest network models have superior accuracy in making diabetes forecasts.