Diabetes Disease Prediction System using Machine Learning Techniques

Nowadays diabetes mellitus is one of the most common chronic diseases worldwide, which is a impacting towards a serious situation in healthcare system. It is an important health issue and it should be managed and diagnosed at and early stage. In previous times various clinical tests were mostly used for diagnosis of diabetes which may be very time taking, costly and had a major dependency on expert interpretation. This issue had to be addressed and solved for a better treatment for those individuals who are suffering from this disease. For solving this problem diabetes disease prediction system is proposed based on machine learning techniques for the use in early detection and accurate diagnosis. This proposed system uses various factors such as body mass index, insulin level, glucose level, family history for optimized prediction. In this proposed system different types of machine learning algorithms have been implemented such as Support Vector Machine, Decision Tree, random Forest, KNN etc. for enhanced identification and effective prediction system. According to experimental results it is demonstrated that higher accuracy, robustness is achieved using ensemble-based models. This system provides a cost effective, reliable and efficient tool for better prediction and improved diagnosis of diabetes. This study highlights the importance and potential of machine learning in enhancing diabetes disease prediction.