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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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Analysis of Erythema Migrans Rashes for Improved Lyme Disease Diagnosis Using Ensemble Machine Learning Techniques Jiwa Permana, Agus Aan
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 1 (2024): 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.v2i1.151

Abstract

This study addresses the challenge of diagnosing Lyme disease through automated classification of Erythema Migrans (EM) rashes, a primary symptom. Employing a Voting Classifier within a k-fold (k=5) cross-validation framework, we developed and validated a model based on a curated dataset of EM rash images and similar dermatological conditions. Image pre-processing involved segmentation and feature extraction using Hu Moments, preparing the data for effective machine learning application. The classifier demonstrated an average accuracy of 81.37%, with variations in precision, recall, and F1-scores across folds, indicative of the model’s robustness and areas for improvement. The results suggest that while the Voting Classifier is a promising tool for Lyme disease diagnosis, further enhancements are required to optimize its diagnostic performance fully. Significant research contributions include the development of a publicly accessible EM rash dataset and the application of ensemble learning techniques to medical image classification, offering a foundation for future advancements in automated disease diagnosis. Recommendations for ongoing research include expanding the dataset diversity and integrating multi-modal clinical data to enhance model accuracy and applicability.
Predictive Modelling of Liver Disease Using Biochemical Markers and K-Nearest Neighbors Algorithm Hidayat, Aidil
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): 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.v2i2.152

Abstract

The incidence of liver cirrhosis-related deaths is on the rise due to increased alcohol consumption, chronic hepatitis infections, and obesity-related liver conditions. Early detection is critical for improving patient outcomes; however, female patients often experience delayed diagnosis. This study aims to develop a predictive model for liver disease using biochemical markers and to investigate gender disparities in diagnostic accuracy. A dataset of 584 patient records from NorthEast Andhra Pradesh, India, was utilized, comprising ten variables per patient, including age, gender, total bilirubin, direct bilirubin, alkaline phosphatase, SGPT, SGOT, total proteins, albumin, and the albumin/globulin ratio. The data were pre-processed by encoding categorical variables and scaling numerical features. The K-Nearest Neighbors (K-NN) algorithm was employed for classification, and performance was evaluated using cross-validation. The model demonstrated variable accuracy across different folds, with accuracy ranging from 57.76% to 73.28%, precision from 58.14% to 70.56%, recall from 57.76% to 73.28%, and F1-score from 57.95% to 70.45%. These results indicate the potential of biochemical markers in predicting liver disease and highlight significant gender disparities in diagnostic accuracy. The study's contributions include the development of a practical predictive tool and the identification of gender-specific diagnostic challenges. Future research should focus on larger, more diverse datasets and explore additional machine learning algorithms to enhance predictive accuracy and address gender disparities in liver disease diagnosis.
Feature Extraction and Classification of Retinal Images Using Sobel Segmentation and Linear SVC Sayyidul Laily , F.Ti Ayyu
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): 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.v2i2.153

Abstract

Eye diseases such as Diabetic Retinopathy, Cataract, and Glaucoma are significant causes of visual impairment and blindness worldwide. Early detection and accurate diagnosis are crucial for effective treatment and management of these conditions. This study aimed to develop a machine learning model for the automated classification of retinal images into four categories: Normal, Diabetic Retinopathy, Cataract, and Glaucoma. The dataset, sourced from Kaggle, comprised approximately 1000 images per class, which were pre-processed using Sobel segmentation to enhance relevant features. Hu Moments were employed for feature extraction due to their invariance to scale, rotation, and translation. The classification was performed using a Linear Support Vector Classifier (SVC), and the model's performance was evaluated through 5-fold cross-validation. The average performance metrics were 44.34% for accuracy, 48.26% for precision, 44.34% for recall, and 41.76% for F1-score. These results indicate that while Sobel segmentation and Hu Moments effectively highlight and capture essential features of retinal images, the Linear SVC classifier's performance is moderate, suggesting the need for more advanced classifiers. The study's findings contribute to the ongoing research in automated eye disease diagnosis by demonstrating the strengths and limitations of classical image processing and machine learning techniques. Future research should focus on exploring more sophisticated models, such as convolutional neural networks, and addressing dataset imbalances to enhance classification accuracy and reliability. This study underscores the potential for automated diagnostic tools in clinical settings but also highlights the necessity for further optimization to achieve practical applicability.
Classification of Skin Diseases using Decision Tree Algorithm on an Imbalanced Dataset Rismayanti, Nurul; Azzahrah, Sitti Fatimah
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): 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.v2i2.155

Abstract

Skin infections caused by pathogens such as bacteria and fungi are common and can lead to serious health complications if not properly managed. Accurate classification of these infections is crucial for effective treatment and management. This study focuses on classifying two skin diseases, Chickenpox and Shingles, using a Decision Tree algorithm applied to an imbalanced dataset sourced from Kaggle. The dataset, which is imbalanced by nature, was split into training (80%) and testing (20%) subsets. Pre-processing involved segmentation using Thresholding to isolate regions of interest and feature extraction using Hu Moments to capture shape characteristics of the lesions. The dataset was scaled to ensure that all features had a mean of 0 and variance of 1. The classifier's performance was evaluated using 5-fold cross-validation, yielding a mean accuracy of 66.06%, with precision, recall, and F1-scores indicating moderate performance. The study highlights the challenges posed by imbalanced datasets and the limitations of the Decision Tree algorithm in this context. The results underscore the importance of proper pre-processing and feature extraction but also suggest the need for more advanced classification techniques and data balancing methods. This research contributes to the field by providing a detailed methodology and comprehensive evaluation metrics, offering insights into the application of machine learning for medical image classification. Future work should focus on improving classifier performance through data augmentation, advanced feature extraction, and exploring other machine learning models better suited for imbalanced datasets.
Optimizing Cardiomegaly Detection: A Random Forest Approach to Processed Chest X-ray Imagery Alfiyyah, Nurul
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): 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.v2i2.156

Abstract

This study explores the application of a Random Forest Classifier for the automated detection of Cardiomegaly from chest X-ray images, utilizing a dataset processed and derived from the NIH Chest X-ray Dataset. Given the crucial need for accurate and timely diagnosis of Cardiomegaly to inform appropriate treatment decisions, this research aims to determine the efficacy of machine learning models in augmenting diagnostic processes. Employing image pre-processing techniques such as Sobel filtering for edge detection and Hu Moments for feature extraction, the study enhances the input features for the model. The performance of the classifier was evaluated using a 5-fold cross-validation approach, yielding results with average accuracy, precision, recall, and F1-scores ranging approximately between 52% and 54%. These findings suggest a moderate level of reliability and consistency, indicating the potential utility of ensemble machine learning methods in medical imaging analysis. However, the variability in performance across different data subsets highlights the challenges and necessitates further optimization. This research contributes to the ongoing discourse on integrating machine learning into clinical settings, demonstrating the potential benefits and current limitations. Future research is recommended to expand the dataset variety, integrate advanced deep learning methodologies, and rigorously test these models in clinical environments. The findings hold significant implications for the development of automated diagnostic tools in healthcare, potentially leading to enhanced diagnostic accuracy and efficiency.
Enhancing Cardiovascular Disease Prediction Accuracy through an Ensemble Machine Learning Approach Ilham, Ilham
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): 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.v2i2.157

Abstract

This study explores the efficacy of an ensemble machine learning approach, specifically a Voting Classifier combining Decision Tree, k-Nearest Neighbors, and Gaussian Naive Bayes classifiers, in predicting cardiovascular diseases (CVDs). Utilizing a dataset consisting of 70,000 clinical records, the model was rigorously tested through 5-fold cross-validation, achieving remarkable results with average accuracies, precision, recall, and F1-scores all exceeding 99%. The findings validate the hypothesis that ensemble models, due to their capacity to leverage multiple learning algorithms, provide superior prediction accuracy and reliability compared to single predictor models. This research not only confirms the effectiveness of ensemble methods in medical diagnostics but also highlights their potential to enhance decision-making in clinical settings. Given the model's success in identifying various stages of cardiovascular conditions with high accuracy, it offers significant implications for early intervention and personalized patient management. Future research should aim to validate these results across more diverse populations and explore the integration of additional predictive factors that could refine the model's applicability. This study contributes to the computational health field by demonstrating how advanced machine learning techniques can be effectively applied in predicting health outcomes.
Predictive Modelling of Chronic Kidney Disease Using Gaussian Naive Bayes Algorithm Azizah, Muthia Febriana; Paramitha, Arimbi Tiara
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): 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.v2i2.160

Abstract

Chronic Kidney Disease (CKD) is a critical global health issue, characterized by significant morbidity and mortality. Early detection is vital for effective management and improved patient outcomes. This study explores the application of the Gaussian Naive Bayes algorithm to predict CKD using a comprehensive dataset from Kaggle, comprising health information from 1,659 patients. The research involves detailed data pre-processing, including feature selection, data scaling, and an 80/20 split for training and testing. The model's performance was evaluated using 5-fold cross-validation, resulting in an average accuracy of 89.93%, precision of 88.15%, recall of 89.93%, and F1-score of 88.42%. These metrics highlight the model's robustness and reliability in identifying CKD cases. Visualizations such as correlation heatmaps, 3D PCA, and t-SNE plots were used to understand feature relationships and data distribution. The results confirm the hypothesis that Gaussian Naive Bayes can effectively predict CKD, providing a reliable tool for early diagnosis. This study contributes to the medical field by demonstrating the utility of machine learning in improving diagnostic accuracy. However, limitations such as dataset biases and the need for comparison with other algorithms are acknowledged. Future research should focus on expanding the dataset, incorporating more features, and exploring additional machine learning models to enhance predictive performance and generalizability. Practical implications suggest that integrating such models into clinical practice could significantly improve patient management and outcomes.
Classification of Pseudopapilledema and Papilledema Using Decision Tree and Hu Moments Hayatou Oumarou
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): 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.v2i2.176

Abstract

Pseudopapilledema, characterized by an anomalous elevation of the optic disc without retinal nerve fiber layer edema, often mimics the presentation of true papilledema caused by increased intracranial pressure. Accurate differentiation between these conditions is critical to avoid unnecessary invasive procedures. This study employs a Decision Tree classifier to classify optic disc images into three categories: normal, papilledema, and pseudopapilledema. The dataset, obtained from Kaggle, consists of imbalanced images segmented using the Canny edge detection method and features extracted using Hu Moments. The dataset was divided into 80% training and 20% testing sets. Performance was evaluated using 5-fold cross-validation, yielding an average accuracy of 53.61%, precision of 55.20%, recall of 54.12%, and F1-score of 55.17%. The study provides a comprehensive analysis of the classifier's performance, including visualizations such as segmentation results, scatter plots of Hu Moments, and confusion matrices. The results indicate that while the Decision Tree classifier demonstrates moderate effectiveness, there is significant room for improvement. The research highlights the potential of machine learning models in medical diagnostics but also underscores the need for more robust algorithms and diverse datasets. Future work should focus on incorporating more complex models and expanding the dataset to enhance diagnostic accuracy. These findings contribute to the field of medical image analysis and propose a non-invasive diagnostic tool that, when integrated with clinical expertise, could improve patient outcomes and reduce unnecessary procedures
Obesity Prediction with Machine Learning Models Comparing Various Algorithm Performances Sulistya, Yudha Islami; Istighosah, Maie
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.v3i1.181

Abstract

Obesity poses a significant global health risk due to its links to conditions such as diabetes, cardiovascular disease, and various cancers, underscoring the need for early prediction to enable timely intervention. This study evaluated the performance of seven machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, ExtraTrees, Gradient Boosting, AdaBoost, and XGBoost—in predicting obesity using health and lifestyle data. The models were assessed based on accuracy, precision, recall, and F1-score, with hyperparameter tuning applied for optimization. The results confirmed that the ExtraTrees Classifier was the best performer, achieving an accuracy of 92.6%, precision of 92.7%, recall of 92.8%, and F1-score of 92.7%. Both Random Forest (91.3% accuracy) and XGBoost (89.9% accuracy) also exhibited strong predictive abilities. In contrast, models like Logistic Regression (74.3% accuracy) and AdaBoost (73.0% accuracy) showed lower effectiveness, emphasizing the advantages of ensemble methods such as ExtraTrees in delivering accurate obesity predictions. These findings suggest that ensemble models provide a promising approach for early diagnosis and targeted healthcare interventions.
Effect of Screen Time on Glaucoma Mahule
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.v3i1.211

Abstract

Glaucoma, characterized by elevated intraocular pressure (IOP) and optic nerve damage, encompasses various types with distinct pathogenic mechanisms. Research has identified key factors influencing glaucoma, such as environmental influences, stress, and age-related factors. This study focuses on the impact of stress on IOP levels in glaucoma patients and evaluates different machine learning (ML) models for enhanced glaucoma detection using OCT and Color Fundus images. Additionally, I explore the environmental implications of elevated IOP, emphasizing lifestyle interventions like yoga to potentially reduce IOP levels. As a practical application, I propose the development of a dedicated mobile app as a digital wellness program for glaucoma patients.

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