Abstract: Diabetes is a long-term metabolic condition characterized by consistently high blood sugar levels. If not properly controlled, it can lead to serious health complications such as cardiovascular diseases, kidney failure, nerve damage, and vision impairment. The rising global incidence of diabetes highlights the urgent need for effective diagnostic methods that enable early detection and proactive management.
Machine Learning (ML) is revolutionizing predictive healthcare by offering a fast, non-invasive, and highly precise approach to assessing diabetes risk. Traditional diagnostic procedures, including fasting blood tests and glucose tolerance tests, often require significant time, clinical visits, and invasive sampling. In contrast, ML-based models can process health data efficiently, recognizing patterns that contribute to a more accurate and timely diabetes risk assessment.
This project aims to develop an ML-powered Diabetes Prediction System that employs various classification algorithms to evaluate an individual's likelihood of having diabetes. Key health factors such as age, glucose concentration, insulin levels, body mass index (BMI), blood pressure, and family medical history are used as predictive features. To ensure high accuracy, the system utilizes multiple machine learning models, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM), allowing for a comparative analysis to determine the most effective approach.
By integrating machine learning into diabetes prediction, this system facilitates early diagnosis, reduces dependence on conventional diagnostic techniques, and supports data-driven medical decision-making. Future improvements may incorporate deep learning models, continuous health monitoring, and wearable device integration to enhance prediction accuracy and improve patient outcomes.
Keywords: Random Forest, Machine learning, support vector machine, Logistic Regression, Logistic Regression
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