Abstract
Abstract: The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest Neighbour, Logistic Regression, Random Forest, Support Vector Machine (SVM), and Decision Tree. The suggested technique works better than conventional methods, providing a more automated and effective method of diabetes detection. It could transform diabetes diagnosis, enhance patient outcomes, and enable individualised treatment plans.
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