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All Journal International Journal of Artificial Intelligence in Medical Issues
Sunia Raharja, I Made
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Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Public Health

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Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Tarigan, Thomas Edyson; Susanti, Erma; Siami, M. Ikbal; Arfiani, Ika; Jiwa Permana, Agus Aan; Sunia Raharja, I Made
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Predicting Cardiovascular Disease Using Machine Learning: A Feature Engineering and Model Comparison Approa Waluyo Poetro, Bagus Satrio; Zulfikar, Dian Hafidh; Sunia Raharja, I Made; Setiohardjo, Nicodemus Mardanus
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.363

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality globally, emphasizing the need for early detection and effective risk stratification. With the increasing availability of clinical and lifestyle-related health data, machine learning (ML) has become a powerful tool to support data-driven diagnosis and decision-making in healthcare. This study aims to develop and evaluate multiple supervised ML models to predict the presence of cardiovascular disease based on non-invasive features obtained from routine medical checkups. The dataset, comprising 69,301 individual records, includes variables such as age, gender, blood pressure, cholesterol, glucose levels, body measurements, and lifestyle habits. Following comprehensive data cleaning and feature engineering such as the derivation of BMI, Mean Arterial Pressure (MAP), and Pulse Pressure four classifiers were applied: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM). Model performance was evaluated using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Among all models tested, the Gradient Boosting Classifier achieved the highest performance, with a ROC-AUC score of 0.8060 and a balanced precision-recall tradeoff, indicating strong discriminatory power. Visualizations such as ROC curves and confusion matrices confirmed the superior capability of Gradient Boosting in differentiating between patients with and without CVD. These findings demonstrate the viability of ML-driven risk assessment models as decision-support tools in clinical settings, potentially aiding in earlier diagnosis and more personalized intervention strategies.
Co-Authors Agus Aan Jiwa Permana Dian Hafidh Zulfikar Erma Susanti I Made Sukarsa Ika Arfiani Juli Astuti, Ni Putu Nicodemus Mardanus Setiohardjo Poetro, Bagus Satrio Waluyo Siami, M. Ikbal Tarigan, Thomas Edyson