A Machine Learning-based Approach to Diabetes Prediction

Abstract

ML-Diabetes is a machine learning-based predictive model for the early detection of diabetes. Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection of diabetes can help prevent its complications and improve patient outcomes. ML-Diabetes is designed to use demographic and clinical data to predict the likelihood of a patient developing diabetes. The model uses a combination of supervised and unsupervised machine learning techniques to analyse and classify data. ML-Diabetes uses a dataset containing demographic and clinical information of patients, including age, sex, BMI, blood pressure, and glucose levels. The dataset is preprocessed and cleaned to remove missing values and outliers. The processed data is then split into training and testing sets, and the model is trained on the training set. The model uses a combination of supervised and unsupervised machine learning techniques, including logistic regression, decision trees, and k-means clustering, to predict the likelihood of a patient developing diabetes. The model is evaluated on the testing set using various performance metrics, including accuracy, precision, recall, and F1-score. The results show that ML-Diabetes is a reliable and accurate predictive model for the early detection of diabetes. The model achieves an accuracy of 85%, precision of 90%, recall of 80%, and F1-score of 85%. The model can be used by healthcare professionals to screen patients for diabetes and provide early interventions to prevent complications.