Diabetes Diagnosis Through Machine Learning: A Comprehensive Patient Classification Method

Innovative methods for the timely and proper diabetes diagnosis are needed, due to the overwhelming prevalence of the condition. This paper presents a new technique for comprehensive patient classification through machine learning and feature utilization which includes the classification of diabetes using various features. The use of advanced state-of-the-art methods of feature selection and a powerful ensemble of independent classifiers improves accuracy, reducing false-positive and false-negative classifications. The model was trained and validated with a dataset that included clinical and demographic parameters from the broader population to ensure it is relevant to many encountered populations. With this, everything else builds its case; benchmarking against other models validates the proposal’s value. What justifies the value of this proposed approach alongside similar methodologies is the accuracy of the model, proving the value of using ensemble stacking techniques. This proof-of-concept emphasizes the impact of advanced machine learning technologies on the healthcare system by promoting customized treatment toward people who are already diagnosed with diabetes, suggesting earlier intervention for emerging complications.