A Smart Healthcare Diabetes Prediction System Using Ensemble of Classifiers

Abstract

Throughout the world, diabetes is a life-threatening disease. This research study aims to develop a smart healthcare machine-learning model for diabetes prediction. The dataset is pre-processed to handle missing data and outliers, and feature selection techniques are used to identify the most relevant variables for the model. An ensemble of classifiers is built by combining logistic regression, XGBoost, random forest, and support vector machine. The performance of the proposed model is assessed using metrics such as accuracy, precision, recall, and F1-score. The results show that the random forest algorithm outperforms other models in terms of accuracy, precision, recall, and F1 score. The model achieves an accuracy of 85%, indicating that it can correctly predict diabetes in 85% of cases. In conclusion, this study demonstrates the feasibility of using machine learning models for diabetes prediction based on patient data. The model can be further improved by incorporating more extensive and diverse datasets and exploring more advanced machine-learning techniques.