Diabetes Classification and Diet Recommendation
Keywords:
DT (Decision Tree), SVM, KNN, Logistic Regression (LR)Abstract
The rising prevalence of diabetes has become a critical subject in healthcare development. Type 2 diabetes, which was once considered a disease of the wealthy, is now affecting millions of people worldwide. Diabetes management is a challenging task that requires patients to monitor blood glucose levels, take medicine, eat a nutritious diet, and exercise frequently. In this research, we investigate diabetes types using Machine Learning Classification algorithms, including Logistic Regression, SVM, Decision Tree, Random Forest, and KNN. Our study aims to provide insights into the effectiveness of these supervised learning algorithms in classifying diabetes types. Additionally, we offer a website that recommends diets based on the level of diabetes. This research aims to contribute to the development of effective diabetes management strategies that can improve patients’ quality of life. Diabetes is associated with a significantly increased risk of developing other diseases such as heart disease, renal disease, vision issues, nerve damage, and so on. Those with uncontrolled diabetes may also have impaired circulation, which causes the blood to circulate more slowly, making it difficult for the body to carry nutrients to wounds and causing the damage to heal more slowly.
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