Optimised Machine Learning Algorithm for Detection And Diagnosis of Diabetes

Diabetes mellitus is a prevalent and chronic metabolic disorder that affects millions of individuals globally. Timely and accurate detection of diabetes is crucial for effective management and prevention of complications. In recent years, machine learning algorithms have demonstrated promising results in various medical domains, including diabetes detection and diagnosis. However, the performance and efficiency of these algorithms heavily depend on appropriate feature selection, algorithm optimization, and high-quality datasets. This research paper presents an optimized machine learning algorithm for the detection and diagnosis of diabetes. The objective is to develop a robust and accurate model that can efficiently identify individuals at risk of diabetes and aid in early diagnosis. The proposed algorithm integrates feature engineering techniques with advanced machine learning algorithms such as support vector machines, random forests, and deep learning networks.. In this approach, a dataset of patients with and without diabetes is collected, preprocessed, and used to train a machine learning model. To achieve high level accuracy in this model we focus on different parameters like body mass index, thickness of skin, genes problem, age factor, retinal image, blood pressure etc. Results shows that this method provide better categorization outcomes then standard methods for a more optimal diabetes diagnosis.