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International Journal of Artificial Intelligence in Medical Issues
Published by yocto brain
ISSN : -     EISSN : 30254167     DOI : https://doi.org/10.56705
Core Subject : Health, Science,
Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Public Health
The International Journal of Artificial Intelligence in Medical Issues (IJAIMI) is a premier, peer-reviewed academic journal dedicated to the integration and advancement of artificial intelligence (AI) in the medical field. The journal aims to serve as a global platform for researchers, clinicians, engineers, and other professionals to share their findings, methodologies, and innovations related to AI application in medical diagnostics, treatment, patient care, and health systems
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 41 Documents
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Classification of Multi-Region Bone Fractures from X-ray Images Using Transfer Learning with ResNet18 Alex, Rasni; Rosmasari
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.v3i1.249

Abstract

Fracture detection in radiographic images is a critical task in orthopaedic diagnostics, often requiring timely and accurate interpretation by medical professionals. However, manual evaluation of X-rays is time-consuming and prone to subjective bias. This study proposes an automated deep learning approach for binary classification of bone fractures using a pre-trained ResNet18 architecture. The model was trained and validated on a multi-region X-ray dataset consisting of 10,580 images categorized into fractured and non-fractured classes. To improve generalization, data augmentation techniques such as rotation and horizontal flipping were applied during pre-processing. The final model achieved a validation accuracy of 97.59%, with high true positive and true negative rates as confirmed by the confusion matrix analysis. The results demonstrate the effectiveness of transfer learning in handling radiographic image classification tasks while maintaining computational efficiency. This research contributes to the development of reliable and scalable computer-aided diagnostic tools that can support clinical decision-making, especially in environments with limited resources.
Predicting Thyroid Cancer Recurrence After Radioactive Iodine Therapy Using Random Forest and Neural Network Models Rosmasari
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.v3i1.250

Abstract

Thyroid cancer recurrence following Radioactive Iodine (RAI) therapy remains a clinical concern, necessitating accurate and timely risk prediction to guide post-treatment management. This study aims to evaluate the effectiveness of machine learning models—Random Forest and Neural Networks—in predicting recurrence using a structured clinical dataset consisting of 383 patient records and 13 diagnostic and pathological attributes. All categorical features were encoded ordinally, and the dataset was partitioned into training and testing sets with appropriate normalization for neural network processing. Both models were evaluated using standard metrics including accuracy, precision, recall, and F1-score. The Random Forest model achieved an accuracy of 97.39%, outperforming the Neural Network which recorded 93.04%. Moreover, Random Forest showed better recall in detecting recurrence cases, making it a more suitable model for clinical application. These results demonstrate that machine learning, particularly ensemble-based methods, can offer a practical and interpretable solution for recurrence prediction, supporting data-driven decision-making in thyroid cancer follow-up care.
Automated Diagnosis of Benign Prostatic Hyperplasia Using Deep Learning on RGB Prostate Images Syafie, Lukman; Rismayanti, Nurul
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.v3i1.251

Abstract

Benign Prostatic Hyperplasia (BPH) is a prevalent non-cancerous enlargement of the prostate gland in aging men, often requiring early diagnosis to prevent urinary complications and improve patient outcomes. Traditional diagnostic procedures are limited by subjectivity and accessibility, especially in under-resourced regions. This study proposes an automated diagnostic approach using a deep learning model based on DenseNet121 to classify RGB prostate images into BPH and normal categories. A region-specific dataset consisting of 176 labeled RGB images, collected from a clinical facility in Bangladesh, was used to train and evaluate the model. Pre-processing included image resizing, normalization, and data augmentation to enhance generalization. Transfer learning was employed to fine-tune the model, which was trained over 10 epochs using the Adam optimizer and cross-entropy loss. The model achieved a best validation accuracy of 94.12%, with a recall of 72.2% for BPH detection, demonstrating its ability to identify pathological patterns in simple imaging modalities. Despite challenges such as dataset size and imbalance, the findings indicate that RGB image-based deep learning models can support clinical diagnosis of BPH in low-resource settings. This work contributes a lightweight, accessible solution for prostate disease screening and provides a foundation for future research on scalable AI-assisted diagnostics.
Ensemble Learning Using KNN and Decision Tree for Virus Infection Classification in Mouse Study Dataset Wahyu Murdiyanto, Aris; Tarigan, Thomas Edyson; Zein, Hamada
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.v3i1.359

Abstract

In this study, we propose an ensemble learning approach to classify viral infection presence in mice using the Mouse Viral Infection Study Dataset. The dataset includes two numerical features—volumes of two administered medications—and a binary label indicating viral presence. To improve prediction performance, we combined K-Nearest Neighbor (KNN) and Decision Tree (DT) classifiers within a soft voting ensemble framework. Standardization was applied as a preprocessing step to ensure fair feature contribution, especially for the distance-sensitive KNN. The ensemble model underwent hyperparameter optimization using GridSearchCV with 5-fold cross-validation to fine-tune the number of neighbors for KNN and depth-related parameters for DT. The experimental results demonstrated that the ensemble classifier achieved perfect performance, with 100% accuracy, precision, recall, and F1-score on the test set. The confusion matrix showed no misclassifications, and the Receiver Operating Characteristic (ROC) curve achieved an Area Under Curve (AUC) of 1.00, indicating excellent separability between classes. These results suggest that the proposed ensemble effectively leverages the strengths of both KNN and DT, making it suitable for biomedical classification tasks where interpretability and reliability are critical. Although the model performed exceptionally well, the simplicity of the dataset, including balanced classes and clear feature boundaries, may have contributed to the ideal performance. Thus, while the findings are promising, further validation is necessary using more complex or noisy datasets. This study contributes a practical, interpretable, and effective ensemble learning framework for binary classification problems in experimental virology, and opens pathways for further research in preclinical biomedical data analytics using hybrid classification systems.
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.
Optimizing Air Quality Index Classification through Multi-Method Machine Learning and Data Balancing Techniques Nuwairy El Furqany
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.v3i2.322

Abstract

Air quality plays a crucial role in safeguarding human health, environmental integrity, and ecosystem sustainability. Accurate assessment of the Air Quality Index (AQI) is essential for effective air quality monitoring and management. However, prior studies often relied on a single machine learning method, which may limit classification performance, especially under class imbalance conditions. This study aims to compare the performance of multiple machine learning algorithms for AQI classification by applying a random oversampling technique to address imbalance among AQI categories. The dataset comprises secondary data on pollutant concentrations (PM10, SO₂, CO, O₃, NO₂) and AQI categories collected from five monitoring stations between 2010 and 2023. Four classification algorithms were evaluated, and performance was measured using accuracy, precision, recall, and F1-score. Before applying random oversampling, the Random Forest model achieved 97.68% accuracy. After oversampling, its performance improved to 99.60%, alongside consistently high precision, recall, and F1-score. Feature importance analysis revealed that ozone (O₃) was the most influential predictor, contributing 67.14% to model decisions. These findings highlight the effectiveness of combining random oversampling with ensemble-based machine learning for highly accurate AQI classification, offering a robust framework for future environmental monitoring applications.
Comparative Machine Learning Models for Dementia Prediction Using SMOTE Puspitasari, Rahma; Amaliah, Tazkirah; Darwis, Herdianti
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.v3i2.351

Abstract

Dementia is a progressive neurodegenerative disorder that leads to cognitive decline and significantly affects patients' quality of life. Early detection is crucial for determining appropriate medical interventions and slowing disease progression. This study aims to develop a machine learning-based dementia prediction model and compare the performance of three algorithms: Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The dataset, obtained from the Kaggle platform, consists of 373 MRI-based patient records categorized into three diagnosis groups: Converted, Demented, and Nondemented. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Experimental results show that the XGBoost algorithm achieved the best performance, with an accuracy of 93.86%, precision of 94%, recall of 94%, and F1-score of 94%, outperforming SVM and Random Forest. The application of SMOTE improved the model’s sensitivity to minority classes. The combination of XGBoost and SMOTE demonstrates high accuracy in dementia prediction and holds potential for integration into clinical decision support systems (CDSS) to assist early diagnosis.
Comparative Study of Machine Learning Methods for Disease Classification Based on Natural Language Symptom Descriptions Jullev Atmadji, Ery Setiyawan; Wibowo, Adityo Permana; Faizal, Edi
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.v3i2.361

Abstract

The growing demand for remote healthcare solutions has increased the importance of efficient disease diagnosis based on textual symptom descriptions. This study explores the application of machine learning models Multinomial Naive Bayes, Random Forest, and Support Vector Machine (SVM) to classify 24 different diseases from natural language symptom inputs. Utilizing a dataset of 1,200 balanced samples and TF-IDF for feature extraction, we trained and evaluated the models using both accuracy and cross-validation metrics. Among the models, SVM achieved the highest test accuracy of 97.5% and demonstrated consistent performance across all disease categories. These findings underscore the potential of classical machine learning approaches in enhancing digital diagnostic tools, particularly for early screening in telemedicine applications. Future work could extend this study by integrating deep learning architectures and multilingual capabilities to accommodate broader and more diverse healthcare scenarios.
Machine Learning-Based Prediction of HIV/AIDS Infection and Treatment Effectiveness: A Clinical Dataset Analysis Jiwa Permana, Agus Aan; Wikranta Arsa, I Gusti Ngurah; Naswin, Ahmad; Sumiyatun
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.v3i2.362

Abstract

The early and accurate prediction of HIV/AIDS infection is critical to improving clinical decision-making and ensuring effective patient management. This study presents a comprehensive machine learning-based approach to predict HIV/AIDS infection status and evaluate the effectiveness of antiretroviral treatments using a well-documented clinical dataset from 1996, comprising 2,139 patient records and 34 features. Through rigorous preprocessing, exploratory data analysis, and feature engineering, several new clinically relevant attributes were constructed, such as CD4/CD8 ratios and immunological change metrics. Four machine learning models—Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting—were trained and evaluated. Among these, the Gradient Boosting classifier achieved the highest ROC-AUC score of 0.9335, while Random Forest provided strong predictive performance with a ROC-AUC of 0.9180 and was selected for further evaluation due to its model transparency. Key features influencing infection prediction included CD4+ and CD8+ dynamics, baseline immunological levels, and treatment history. Additionally, the study examined treatment effectiveness by analyzing CD4+ cell count responses across different therapy types. The combination of ZDV and ddI emerged as the most effective regimen, improving immune outcomes and lowering infection rates, while ZDV monotherapy showed the least favorable results. This work underscores the potential of machine learning as a clinical decision support tool in HIV/AIDS care and provides data-driven insights into treatment optimization. Future studies should incorporate longitudinal patient data and real-world clinical environments for broader applicability.
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.

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