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All Journal INSTALL: Information System and Technology Journal
Anggara Kurniawan
Sistem Informasi, Jurusan Matematika dan Teknologi Informasi, Institut Teknologi Kalimantan
Computer Science & IT Decision Sciences, Operations Research & Management Education

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The Diabetes Prediction Using Flask and Decision Tree Classifier with Cross-Validation: Prediksi Diabetes Menggunakan Flask dan Decision Tree Classifier dengan Validasi Silang Nor Anisa; Anggara Kurniawan
INSTALL: Information System and Technology Journal Vol 1 No 1 (2024): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v1i1.548

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 user-friendly 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.
Co-Authors Nor Anisa