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Prediction of diabetes using machine learning algorithms and comparison with other algorithms

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Mr.C.Iyyappan, Dr.R.Latha
» doi: 10.31838/ecb/2023.12.si6.440


Diabetes mellitus (DM) has numerous long-term effects on health. Diabetes may be caused by ageing, insufficient exercise, a sedentary lifestyle, a familial history of diabetes, high blood pressure, sadness or tension, poor diet, etc. Diabetes-related complications such as cardiovascular disease, diabetic neuropathy, diabetic retinopathy, diabetic nephropathy, and dementia are more prevalent in diabetics. Those between 25 and 74 years old are the most vulnerable. Diabetes complications may be severe if the condition is not properly diagnosed and managed. People are prevented from valuing their health by their frantic schedules. Right away following consuming sustenance, glucose is discharged into circulation. When blood glucose levels are excessively elevated, the pancreas secretes insulin. Without insulin, it is difficult for glucose to enter cells and be used as fuel. Diabetes can be detected early on, which aids in maintaining a wholesome lifestyle. Programmed based on machine learning will be successful because they can be taught and evaluated using massive amounts of data and are able to enhance themselves by generating future predictions. We present the results of training multiple algorithms on the data gathered for this article. SVM generated the most consistent results among the three evaluated algorithms

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