Prediction of Diabetes Using Machine Learning

This research seeks to compare the overall health of diabetic patients with that of their healthy counterparts based on various heath indicators obtained from a large dataset comprising of pregnancies, glucose levels, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function and age. Relative to the healthy group, increased average glucose level, higher BMI, and older age are identified in the diabetic participants. Most importantly, the results show that obesity is a significant risk factor for diabetes, and many diabetics have BMIs that are elevated above healthy levels. The data also points towards the fact that insulin resistance and prevalence of diabetes are related to each other, as exemplified by the range of insulin variation in type 2 diabetes subjects. Thus, the study also underscores the significance of family history and age in the development of diabetes, underlining the need for people with diabetes risk factors to undergo screening as soon as possible. The findings revealed an accuracy of close to 82% in diagnosing the condition from the dataset whose algorithms inform machine learning models that are already applied in clinical practice. These findings attest to the complex nature of the diseases and emphasize the importance of integrating various concepts and thinking in relation to diabetes risk factors to help ensure early detection and successful management of the conditions. In conclusion, this research enhances the literature on the application of health data analytics in enhancement of diabetes prevention and management.