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All Journal PROCEEDING IC-ITECHS 2014 JURNAL MEDIA INFORMATIKA BUDIDARMA
Tajuddin, Taniza
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Journal : PROCEEDING IC-ITECHS 2014

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SHIELD: Symptom-Based Hybrid Intelligent Early Learning for Disease Prediction Fazil Akashah, 'Asrul 'Azeem; Tajuddin, Taniza
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1677

Abstract

Traditional diagnostic approaches often face delays and inaccuracies, while standalone machine learning models fail to account for individual uniqueness. The SHIELD system leverages hybrid machine-learning models to enhance disease prediction based on patient symptoms. This study integrates Gradient Boosting, Decision Trees, and Random Forest models, combining their strengths using an ensemble voting approach. A comprehensive dataset from Kaggle, enriched with symptom severity mappings, enables accurate and personalized predictions. The system delivers practical outputs, including disease names, descriptions, and home remedies, through a user-friendly web interface. Achieving an accuracy of approximately 99.59% with the ensemble model, SHIELD demonstrates its potential to revolutionize early disease detection, aligning with global health objectives.
Co-Authors Ahmad, Khairul Adila Aminuyati Fazil Akashah, 'Asrul 'Azeem Moch Yusuf Asyhari Pratiwi Susanti Shamsuddin, Norin Rahayu