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All Journal JATI (Jurnal Mahasiswa Teknik Informatika)
Hakeel, Mohamed
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Computer Science & IT

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DIABETES PREDICTION MACHINE LEARNING-BASED DIABETES PREDICTION APP USING RANDOM FOREST ALGORITHM Hakeel, Mohamed
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12654

Abstract

Diabetes is a chronic metabolic condition that causes increased blood glucose levels in millions of people worldwide. Early detection and quick action are critical for controlling this illness and preventing consequences. This work describes creating a user-friendly smartphone application for diabetes prediction using the Random Forest algorithm, a powerful machine-learning technique. The software uses user-provided information, such as age, body mass index (BMI), blood pressure, and glucose levels, to forecast the chance of acquiring diabetes. The Random Forest model was trained on a large dataset of medical records and achieved an astounding 88% accuracy on the test set. The app, created using Python and Figma, a cross-platform framework, has an intuitive and user-friendly design that allows users to enter personal information and obtain immediate forecasts. The app is a useful screening tool, allowing people to estimate their risk of getting diabetes and receive necessary medical assistance as soon as possible. The successful implementation of this diabetes prediction software highlights machine learning algorithms' potential to improve preventive healthcare and promote early intervention for chronic diseases.
SYMPTOM-BASED DISEASE PREDICTION USING MACHINE LEARNING Hakeel, Mohamed; Primajaya, Aji; Nurfikli, E.Haodudin
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14087

Abstract

There are now more opportunities to increase diagnostic accessibility and accuracy thanks to the application of machine learning (ML) in healthcare, especially in environments with limited resources. The Random Forest Classifier (RFC) and Multi-Layer Perceptron (MLP) models emphasize this study's strong framework for symptom-based disease prediction utilizing machine learning methods. Our approach emphasizes the significance of data preparation, feature engineering, and model evaluation while addressing important issues, including missing data, symptom overlap, and ethical implications using Kaggle datasets. According to our findings, the RFC model performs better than the MLP classifier, with 99% accuracy. We also created an interactive platform for disease prediction, data addition, and model retraining using a web application built using Streamlit. Especially in poverty-stricken areas, this approach provides a scalable and dependable tool for early disease diagnosis, lowering diagnostic mistakes and enhancing healthcare accessibility.
Co-Authors Nurfikli, E.Haodudin