Paper Title
DIABETES PREDICTION USING MACHINE LEARNING ALGORITHMS LIKE SVM, NB AND LGBMAbstract
Diabetes mellitus (DM) is a chronic disease that is considered to be life-threatening. It can affect any part of the body over time, resulting in serious complications such as nephropathy, neuropathy, and retinopathy. In this work, several supervised classification algorithms were applied for building different models to predict and classify eight diabetes complications. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. In this paper, we use supervised machine-learning algorithms like Support Vector Machine (SVM), Naive Bayes classifier and Light GBM to train on the actual data of 520 diabetic patients and potential diabetic patients aged 16 to 90. Through comparative analysis of classification and recognition accuracy, the performance of support vector machine is the best.
KEYWORDS : Diabetes, Machine, Learning, Prediction, Dataset,Support vector machine.