DIABETES PREDICTION USING MACHINE LEARNING

SEEE DIGIBOOK ON ENGINEERING & TECHNOLOGY, VOL. 01, APRIL 2019 PP.(107-110)

Abstract– Diabetes is a chronic illness that has the potential to precipitate a global health care crisis. According to the International Diabetes Federation, 382 million people worldwide are living with diabetes. This will double to 592 million by 2035. Diabetes is a condition caused by a rise in blood glucose levels. This elevated blood glucose causes frequent urination, increased thirst, and increased appetite. Diabetes is a major cause of blindness, kidney failure, amputations, heart failure, and stroke. When we consume food, our bodies convert it into sugars, or glucose. At that point, our pancreas should secrete insulin. Insulin functions as a key to unlock our cells, allowing glucose to enter and allowing us to use it for energy. However, with diabetes, this method is ineffective. Diabetes types 1 and 2 are the most prevalent forms of the disease, although there are additional types, such as gestational diabetes, which occurs during pregnancy, and others. Machine learning is an emerging topic of data science concerned with how machines acquire knowledge via experience. By merging the findings of several machine learning approaches, the goal of this study is to produce a system that can conduct more accurate early diabetes prediction for a patient. K closest neighbour, Logistic Regression, Random forest, Support vector machine, and Decision tree are utilised as algorithms. The accuracy of the model utilising each algorithm is determined. The model with the highest accuracy is then selected to forecast diabetes.

Index Terms – Keyword -Data Security, Cloud, Integrity, Bulk Request, Bulk Response, and Dynamic;

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Bharathi S, Aravind A, Deepika E, Ganesh P, Shravan D
Department of Information Technology,
Rathinam Technical Campus,
Coimbatore, India

DIABETES PREDICTION USING MACHINE LEARNING

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