Abstract
Diabetes is a metabolic disorder characterized by a malfunction in insulin release, resulting in a rise in blood sugar levels in the body. Diabetes diagnosis must be made on time and precisely in order to be effective and enhance patient outcomes. The diabetes management presents a formidable challenge in modern healthcare, demanding a combination of timely interventions, precise data analysis, and personalized medical services. Furthermore, there exists a growing demand for advanced predictive models that not only provide accurate forecasts but also offer transparency and interpretability. The study objectives are to develop an innovative machine learning model for data-driven diabetes prediction and medical services. To identify the machine learning model that best suits the proposed system, a literature review related to different methods used in diagnosing diabetes is conducted. The merits and demerits of each existing system were identified to devise a proposed model that seamlessly integrates data from various sources, including blood glucose levels and patient health information. Based on the review, the study suggests an innovative machine learning model that utilizes the Explainable Decision Tree Model, leveraging cloud computing to analyse diabetes patient data and make predictions. The integration of cloud computing allows for seamless data integration from various sources. This research project represents a significant step forward in personalized diabetes care, enabling patients to proactively manage their condition while providing healthcare professionals with a powerful tool for delivering tailored medical services.
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