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All Journal Heca Journal of Applied Sciences Malacca Pharmaceutics Infolitika Journal of Data Science Indonesian Journal of Case Reports Grimsa Journal of Business and Economics Studies
Idroes, Ghifari Maulana
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Public Health Social Sciences

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Journal : Infolitika Journal of Data Science

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Advanced Anemia Classification Using Comprehensive Hematological Profiles and Explainable Machine Learning Approaches Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Suhendra, Rivansyah; Bakri, Tedy Kurniawan; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.237

Abstract

Anemia is a common health issue with serious clinical effects, making timely and accurate diagnosis essential to prevent complications. This study explores the use of machine learning (ML) methods to classify anemia and its subtypes using detailed hematological data. Six ML models were tested: Gradient Boosting, Random Forest, Naive Bayes, Logistic Regression, Support Vector Machine, and K-Nearest Neighbors. The dataset was preprocessed using feature standardization and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Gradient Boosting delivered the highest accuracy, sensitivity, and F1-score, establishing itself as the top-performing model. SHapley Additive exPlanations (SHAP) analysis was applied to enhance model interpretability, identifying key predictive features. This study highlights the potential of explainable ML to develop efficient, accurate, and scalable tools for anemia diagnosis, fostering improved healthcare outcomes globally.
Inductive Biases in Feature Reduction for QSAR: SHAP vs. Autoencoders Noviandy, Teuku Rizky; Idroes, Ghifari Maulana; Lala, Andi; Helwani, Zuchra; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 3 No. 1 (2025): May 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i1.306

Abstract

Machine learning models in drug discovery often depend on high-dimensional molecular descriptors, many of which may be redundant or irrelevant. Reducing these descriptors is essential for improving model performance, interpretability, and computational efficiency. This study compares two widely used reduction strategies: SHAP-based feature selection and autoencoder-based compression, within the context of Quantitative Structure-Activity Relationship (QSAR) classification. LightGBM is used as a consistent modeling framework to evaluate models trained on all descriptors, the top 50 and 100 SHAP-ranked descriptors, and a 64-dimensional autoencoder embedding. The results show that SHAP-based selection produces interpretable and stable models with minimal performance loss, particularly when using the top 100 descriptors. In contrast, the autoencoder achieves the highest test performance by capturing nonlinear patterns in a compact, low-dimensional representation, although this comes at the cost of interpretability and consistency across data splits. These findings reflect the differing inductive biases of each method. SHAP prioritizes sparsity and attribution, while autoencoders focus on reconstruction and continuity. The analysis emphasizes that descriptor reduction strategies are not interchangeable. SHAP-based selection is suitable for applications where interpretability and reliability are essential, such as in hypothesis-driven or regulatory settings. Autoencoders are more appropriate for performance-driven tasks, including virtual screening. The choice of reduction strategy should be guided not only by performance metrics but also by the specific modeling requirements and assumptions relevant to cheminformatics workflows.
Co-Authors Adi Purnawarman, Adi Amalina, Faizah BAKRI, TEDY KURNIAWAN Dian Handayani Dian Lestari, Nova El-Shazly, Mohamed Essy Harnelly Fauzi, Fazlin Mohd Hafizah, Iffah Idroes, Ghalieb Mutig Imran Imran Irma Sari Irsan Hardi Isnaini, Nadia Kadri, Mirzatul Kemala, Pati Khairan Khairan Lala, Andi Maimun Syukri, Maimun Maulydia, Nur Balqis Mohd Fauzi, Fazlin Rinaldi Idroes Ringga, Edi Saputra Salsabila, Indah Solly Aryza Suhendra, Rivansyah Teuku Rizky Noviandy TRINA EKAWATI TALLEI Zuchra Helwani, Zuchra