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All Journal Jurnal Buana Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika U Karst JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Informatika Universitas Pamulang Jiko (Jurnal Informatika dan komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Building of Informatics, Technology and Science Buletin Ilmiah Sarjana Teknik Elektro Jurnal Informatika Ekonomi Bisnis Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Yumary: Jurnal Pengabdian kepada Masyarakat International Journal of Robotics and Control Systems KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Brilliance: Research of Artificial Intelligence Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis Jurnal Pengabdian Masyarakat Charitas Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Airlangga, Gregorius
Atma Jaya Catholic University Of Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Evaluating Machine Learning Models for Mental Health Diagnostics: A Comparative Analysis and Visual Insights Airlangga, Gregorius
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1702

Abstract

This study addresses the critical challenge of enhancing mental health diagnostics amidst a surge in global mental disorder prevalence. With mental health conditions predicted to become the leading cause of disability by 2030, there is an urgent need for more effective diagnostic methods that transcend the limitations of traditional frameworks, such as subjectivity and clinician bias. Leveraging the capabilities of machine learning (ML) to analyze complex datasets, this research aims to fill the gap in the comparative effectiveness of various ML models, particularly within the context of imbalanced mental health datasets. We systematically evaluated the performance of diverse ML models—including Random Forest, Gradient Boosting, Support Vector Machines, and others—on a rich dataset embodying a wide spectrum of symptoms and diagnoses. Through advanced data preprocessing techniques, such as innovative handling of missing values and categorical encoding, coupled with RandomizedSearchCV for model optimization, we provided a comprehensive analysis of the models' effectiveness. The application of oversampling strategies addressed the challenge of dataset imbalance, ensuring realistic clinical scenario evaluations. The study's findings are presented through detailed model performance metrics and visual analytics, such as symptom distribution visualizations and correlation cluster maps, enhancing interpretability and clinical relevance. The discussion section explores the practical applicability of these findings in clinical settings, acknowledging limitations and outlining future research directions. In conclusion, the study presents a nuanced narrative of ML model selection and performance evaluation complexities. The superior performance of ensemble methods like Random Forest and Gradient Boosting classifiers for certain diagnoses demonstrates the potential of ML in mental health diagnostics. However, the varied performance across models underscores the importance of context-specific model selection, considering the trade-offs between accuracy, interpretability, and computational efficiency. This research contributes significantly to the field of mental health diagnostics by highlighting models with the greatest promise for clinical application and by providing a framework for future advancements integrating ML into mental health diagnostics.
Comparative Analysis of Neural Network Architectures for Mental Health Diagnosis: A Deep Learning Approach Airlangga, Gregorius
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1703

Abstract

Mental health conditions present a complex diagnostic challenge due to the subtlety and diversity of symptoms. This study provides a comprehensive analysis of various neural network architectures, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory networks (LSTM), and Dense Neural Network (DNN), in their ability to classify mental health conditions. Utilizing a rich dataset of symptoms and expert diagnoses, we preprocessed the data to address class imbalances and trained each model to evaluate its diagnostic performance. Our results are presented through confusion matrices that reveal the accuracy, precision, recall, and F1-scores for each model. The MLP and DNN models demonstrated high accuracy in identifying distinct conditions but struggled with overlapping symptoms. LSTM and RNN models captured temporal patterns to some extent yet required further optimization for improved accuracy. CNN models showed robust feature detection capabilities, with the CNN 1D model excelling in specificity for certain conditions. However, a common challenge across all models was the differentiation between conditions with similar symptom presentations. Our findings suggest that while individual models have their strengths, an ensemble approach may be necessary for enhanced diagnostic precision. Future work will focus on integrating models, refining feature extraction, and employing explainable AI to increase transparency and trust in model predictions. Additionally, expanding the dataset and conducting clinical trials will ensure the models' effectiveness in real-world settings. This research moves us closer to achieving nuanced, AI-driven diagnostics that can support clinicians and benefit patient outcomes in mental healthcare.
Co-Authors Afandi Nur Aziz Thohari, Afandi Nur Aziz Alamsyah - Amalia, Andika Elok Basuki, Widodo Widjaja Bata, Julius Victor Manuel Christanto, Henoch Juli Denny Jean Cross Sihombing Denny Jean Cross Sihombing Eugenius Kau Suni Henoch Juli Christanto Julius Bata Julius Victor Manuel Bata Kristian, Yoel Lenson, Abraham K. S. Lim, Bobi Hartanto Pramudita Makin, See Jong Nugroho, Oskar Ika Adi Pedro Manuel Lamberto Buu Sada Philia, Gregoria Piolo, Samuel Pujo Hari Saputro Rahmananta, Radyan Ronald Sukwadi Setiawan, Yohanes Evan Sidhunata, Billy Macarius Stephen Aprius Sutresno Stephen Aprius Sutresno, Stephen Aprius Sugianto, Lai Ferry Suni, Eugenius Kau