Sugar D- A machine learning-based application to predict diabetes type and suggest diet plans for diabetic patients based on type.

Abstract

"Diabetes's increasing prevalence needs the development of robust predicting technologies as well as tailored therapies. This study uses a machine learning framework to tackle the complex job of detecting diabetes type and developing individualized food regimens for individual diabetic patients. The vital need for precise diabetes type prediction and personalized dietary recommendations is emphasized. The study uses the Random Forest method for its robustness and incorporates important characteristics such as gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, and glucose levels into the entire feature set. Model training entails meticulously splitting the dataset into discrete training and testing sets to guarantee the proposed approach's dependability. Preliminary data are reported using quantitative indicators such as the confusion matrix, classification report, and accuracy score. These measures provide subtle insights into the model's performance, demonstrating its ability to appropriately categorize various diabetes kinds. The provided accuracy score serves as an initial benchmark, indicating the model's ability to predict diabetes kinds using the supplied variables."