Diabetes Prediction Using Flask and Decision Tree Classifier with CrossValidation

Nor, Anisa and Anggara, Kurniawan (2024) Diabetes Prediction Using Flask and Decision Tree Classifier with CrossValidation. [Karya Dosen UNISM]

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Abstract

Diabetes is a chronic medical condition that impairs the body's ability to process blood sugar, leading to elevated levels of glucose in the blood. This condition can cause serious health complications if not managed properly. Early detection and intervention are crucial in preventing these complications. This study aims to develop a userfriendly web application using Flask, a lightweight Python web framework, to predict the type of diabetes based on symptoms reported by users. The Machine Learning model utilized for this purpose is the Decision Tree Classifier, chosen for its simplicity and interpretability. The model's performance was evaluated through cross-validation to ensure reliability and accuracy. The results demonstrate that the developed application can effectively predict the type of diabetes, providing valuable insights and assisting users in seeking timely medical advice. This tool has the potential to enhance public awareness about diabetes and facilitate early diagnosis, ultimately contributing to better health outcomes for individuals at risk of this condition.

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