Machine Learning based approach for Diabetes Prediction

Abstract

Diabetes is an illness brought on by an excessive amount of glucose within the body. Ignorance of diabetes is no longer acceptable. If neglected, it may also result in more severe health concerns for a person, such as heart-related problems, renal problems, blood pressure, eye damage, and effects on other body organs. Insulin hormone is affected, which leads to abnormal crab metabolism and elevates blood sugar levels. According to the World Health Organization, 422 million people worldwide suffer with diabetes. low- and middle-class people being disproportionately affected. The condition is caused by the body producing insufficient amounts of insulin. Additionally, this might reach 490 billion by 2030. To benefit from this challenging job, we may apply ensemble techniques and system learning for classification on this image to forecast whether diabetes will be present in a dataset. When comparing one version to another, the accuracy varies depending on the model. The assignment provides the accurate or improved accuracy version, indicating that the model can effectively predict diabetes. Our findings demonstrate that random forested areas outperformed other system mastery strategies in terms of accuracy. Keywords: Classification Algorithms, Supervised Learning, Unsupervised Learning, Random forest