DIABETES PREDICTION USING MACHINELEARNING

Abstract

Abstract - Diabetes is a chronic condition that could lead to a global health crisis. The International Diabetes Federation estimates that 382 million people worldwide have diabetes. By 2035, this will have doubled to 592 million. A condition known as diabetes is brought on by high blood glucose levels. The symptoms of increased thirst, hunger, and frequency of urination are brought on by this elevated blood glucose. One of the primary causes of renal failure, blindness, amputations, heart failure, and stroke is diabetes. Our bodies convert food into sugars, or glucose, when we consume. Our pancreas is meant to release insulin at that point. Insulin functions as a key to unlock our cells, letting glucose in and enabling us to utilise it as fuel. But this system is ineffective in the case of diabetes. Although type 1 and type 2 diabetes are the most common forms, there are other kinds as well, such as gestational diabetes, which appears during pregnancy. In data science, machine learning is a young scientific discipline that studies how machines pick up knowledge via experience. The purpose of this research is to develop a system that can more correctly detect diabetes in individuals at an early stage by combining the results of many investigations. Machine learning techniques. There is usage of methods such as K closest neighbour, decision tree, random forest, logistic regression, and support vector machine. It is computed what the model's accuracy is while employing each of the algorithms. The model that predicts diabetes isthen chosen based on the best accuracy then chosen based on the best accuracy. The algorithms. The model that predicts diabetes is then chosen based on the best accuracy. The model's accuracy is while employing each of the algorithms. The model that predicts diabetes is then chosen based on the best accuracy.is usage of methods such as K closest neighbour, decision tree, random forest, logistic regression, and support vector machine. It is computed what the model's accuracy is while employing each of the algorithms. The model that predicts diabetes is then chosen based on the best accuracy.pregnancy.In data science, machine learning is a young scientific discipline that studies how machines pick up knowledge via experience.The purpose of this research is to develop a system that can more correctly detect diabetes in individuals at an early stage by combining the results of many investigations.Machine learningtechniques. There is usage of methods such as K closest neighbour, decision tree, random forest, logistic regression, and support vector machine. It is computed what the model's accuracy is while employing each of the algorithms. The model that predicts diabetes is then chosen based on the best accuracy. Keywords: Decision tree, K closest neighbour, machine learning, diabetes, logistic regression, support vector machines, and accuracy.