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All Journal International Journal of Artificial Intelligence in Medical Issues
Siami, M. Ikbal
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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 Hair Loss with Machine Learning: A Multi-Factor Analysis Siami, M. Ikbal; Azis, Huzain
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (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.v3i1.360

Abstract

Hair loss is a multifactorial condition influenced by genetics, hormonal imbalance, lifestyle choices, and environmental factors. This study investigates the potential of machine learning (ML) to predict hair loss using a diverse dataset comprising categorical and numerical indicators related to these contributing variables. We applied an extensive data preprocessing pipelineincluding handling missing values, frequency encoding, and engineered interaction featuresto improve model input quality. Five ML algorithms (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost) along with an ensemble voting classifier were trained and evaluated on a balanced dataset. While performance metrics such as accuracy and F1-score remained modest, with the highest values around 50%, the analysis revealed the prominent role of age, stress, and nutritional deficiency in hair loss. Despite the limited predictive capability of the current feature set, this study presents a reproducible framework for ML-driven health diagnostics and identifies key directions for future work. Enhancing data granularity and incorporating richer clinical inputs could significantly boost prediction accuracy in subsequent studies.
Co-Authors Agus Aan Jiwa Permana Erma Susanti Ika Arfiani Sunia Raharja, I Made Tarigan, Thomas Edyson