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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Fatma Indriani
Department of Computer Science, Lambung Mangkurat University, Banjarbaru, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Deep Learning-Based Lung Sound Classification Using Mel-Spectrogram Features for Early Detection of Respiratory Diseases Midfai Yabani; Mohammad Reza Faisal; Fatma Indriani; Dodon Turianto Nugrahadi; Dwi Kartini; Kenji Satou
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1256

Abstract

Respiratory diseases such as asthma, chronic obstructive pulmonary disease, and pneumonia remain among the leading causes of death globally. Traditional diagnostic approaches, including auscultation, rely heavily on the subjective expertise of medical practitioners and the quality of the instruments used. Recent advancements in artificial intelligence offer promising alternatives for automated lung sound analysis. However, audio is an unstructured data format that must be converted into a suitable format for AI algorithms. Another significant challenge lies in the imbalanced class distribution within available datasets, which can adversely affect classification performance and model reliability. This study applied several comprehensive preprocessing techniques, including random undersampling to address data imbalance, resampling audio at 4000 Hz for standardization, and standardizing audio duration to 2.7 seconds for consistency. Feature extraction was then performed using the Mel Spectrogram method, converting audio signals into image representations to serve as input for classification algorithms based on deep learning architectures. To determine optimal performance characteristics, various Convolutional Neural Network (CNN) architectures were systematically evaluated, including LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-50, and ResNet-152. VGG-16 achieved the highest classification accuracy of the tested models at 75.5%, demonstrating superior performance in respiratory sound classification tasks. This study demonstrates the potential of AI-based lung sound classification systems as a complementary diagnostic tool for healthcare professionals and the general public in supporting early identification of respiratory abnormalities and diseases. The findings suggest that automated lung sound analysis could enhance diagnostic accessibility and provide more valuable support for clinical decision-making in respiratory healthcare applications
Optimizing Input Window Length and Feature Requirements for Machine Learning-Based Postprandial Hyperglycemia Prediction Muhammad Rafly Alfarizqy Maulana; Fatma Indriani; Friska Abadi; Dwi Kartini; Muhammad Itqan Mazdadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1401

Abstract

Continuous glucose monitoring systems currently generate alerts only after blood glucose thresholds are breached, limiting their utility for proactive diabetes management. Predicting postprandial glucose excursions before they occur requires determining the optimal amount of historical data and identifying which features contribute most to prediction accuracy. This study systematically evaluates how the length of the pre-meal observation window and feature composition affect machine-learning predictions of hyperglycemia events 60 minutes after eating. We analyzed 1,642 meal events from 45 adults wearing continuous glucose sensors, constructing features from pre-meal glucose trajectories, meal macronutrients, time of day, and health status. Four observation windows (15, 30, 45, 60 minutes) and three feature sets (all features, glucose-only, meal-only) were evaluated using Random Forest, XGBoost, and CatBoost with 5-fold group cross-validation. CatBoost with a 30-minute window achieved the best performance: 72.6% F1-macro, 79.6% accuracy, and 64.0% recall for hyperglycemia detection. Extending windows beyond 30 minutes did not yield consistent benefits, whereas 15-minute windows yielded comparable results. Glucose trajectory features alone retained 94% of full model performance (68.5% F1-macro), whereas meal composition alone proved insufficient (59.4% F1-macro). These findings demonstrate that recent glucose history dominates short-term prediction, enabling practical real-time systems with minimal data requirements. A 30-minute observation window with glucose and meal features offers an effective balance between prediction accuracy and system responsiveness.
Co-Authors Dodon Turianto Nugrahadi Dwi Kartini Friska Abadi Kenji Satou Midfai Yabani Mohammad Reza Faisal Muhammad Itqan Mazdadi Muhammad Rafly Alfarizqy Maulana