Preliminary Diagnosis of Diabetes Through Comparative Analysis of Supervised Machine Learning Techniques

When it comes to medical studies and the life sciences, Machine Learning already made a significant impact. The metabolic disease known as diabetes is characterized by continuously high blood sugar levels that do not respond normally to insulin. Early diagnosis of diabetes helps to maintain a healthy lifestyle. The article’s content has centered on analyzing PIMA dataset-based diabetes patients and developing a machine learning-based detection model with minimal dependencies. Machine learning (ML) algorithms will be an effective strategy because they can be trained and tested using large amounts of data and can further improve themselves by making predictions. Several algorithms, including Gradient Boosting, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes, are trained using our collected dataset in this article. Random Forest’s prediction results are shown to be the most accurate after being compared to those of the other algorithms.