cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Huzain Azis
Contact Email
huzain.azis@umi.ac.id
Phone
+628114484875
Journal Mail Official
ijaimi.journal@gmail.com
Editorial Address
Jln. Paccerakkang Daya No.140, Kel. Berua Kec. Biringkanaya, Makassar, Sulawesi Selatan, Indonesia
Location
Unknown,
Unknown
INDONESIA
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
 1   2   3   4   5 
Advancing Healthcare Diagnostics: A Study on Gaussian Naive Bayes Classification of Blood Samples As'ad, Ihwana
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): 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.v1i2.120

Abstract

This research paper presents a comprehensive analysis of the Gaussian Naive Bayes (GNB) classifier's application in predicting health conditions from blood samples, underpinned by a handcrafted dataset representative of typical physiological ranges. Through a meticulous 5-fold cross-validation approach, the study assesses the GNB model's performance in terms of accuracy, precision, recall, and F1-score, revealing not only high efficacy but also consistent improvement in predictive capability across successive folds. A detailed confusion matrix provides further insights into the model's classification proficiency. The results affirmatively address the research hypotheses, indicating the GNB classifier's reliability and effectiveness as a diagnostic tool. With the increasing need for rapid and accurate medical diagnostics, the study's findings underscore the potential of even simple machine learning models to augment traditional blood test analyses, thereby offering significant contributions to the field of biomedical informatics. The research lays the groundwork for future explorations into the integration of machine learning in clinical settings, advocating for the verification of these promising results with real-world clinical data and the comparative analysis of various machine learning models. The potential for automated, precise diagnostic processes paves the way for enhanced patient care and resource optimization in healthcare.
Enhancing Gastrointestinal Disease Diagnosis with KNN: A Study on WCE Image Classification Randi Rizal
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): 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.v1i1.133

Abstract

This study explores the application of the K-Nearest Neighbors (KNN) algorithm, following Sobel segmentation and Hu Moment feature extraction, to classify Wireless Capsule Endoscopy (WCE) images into Normal and Ulcerative Colitis conditions. Through a rigorous 5-fold cross-validation approach, the research aimed to determine the KNN algorithm's accuracy, precision, recall, and F1-score on the WCE Curated Colon Disease Dataset. The findings revealed high performance across all metrics, with accuracy rates extending up to 90.625%. The confusion matrix provided further validation, illustrating a high true positive rate coupled with a low false negative rate. These results substantiate the hypothesis that employing edge detection and shape descriptors as pre-processing techniques can significantly enhance the efficacy of machine learning algorithms in medical image classification. The study’s contribution is twofold: it reaffirms the potential of machine learning in the advancement of medical diagnostics and provides a methodological framework for automated image classification that can assist clinicians. It is recommended that future research extends to broader datasets and explores various algorithms to enhance diagnostic precision. In practice, integrating this research into a clinical decision support system could revolutionize diagnostic processes, offering a non-invasive, accurate, and efficient tool for gastroenterological diagnostics.
Exploring the Acceptance of Artificial Intelligence in Healthcare in Saudi Arabia Althubaiti, Haytham; Ali Sulaiman A. Al Yousef
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.v2i1.135

Abstract

The integration of artificial intelligence (AI) into various sectors has garnered global attention, notable within the healthcare domain. In Saudi Arabia, discussions surrounding AI’s application in healthcare have been particularly pronounced, highlighted at significant gathering during the World Economic Forum in the nation’s capital. This paper aims to explore the multifaceted incorporation of AI into medical practices in Saudi Arabia, with a focus on enhancing healthcare delivery. Drawing upon insights from cultural anthropology and medicine, this study illuminates key aspects of AI adoption among Saudi medical professionals. Despite growing interest, there remains a dearth of comprehensive studies assessing AI acceptance, readiness, and proficiency among healthcare personnel, necessitating larger-scale investigations for more accurate insights. Current literature suggests that while some practitioners have embraced AI, many lack formal education and exhibit apprehension towards its utilization. Consequently, there is a pressing need for undergraduate and postgraduate educational programs tailored to AI integration within Saudi Arabia’s healthcare system. Such initiatives not only empower practitioners to harness AI’s full potential but also address concerns and apprehensions, particularly among senior professionals. By fostering a culture of AI education and proficiency, Saudi Arabia can effectively leverage AI to enhance healthcare outcomes and address emerging challenges in the medical landscape.
Assessing the Performance of Logistic Regression in Heart Disease Detection through 5-Fold Cross-Validation Azis, Huzain
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.v2i1.137

Abstract

This study explores the effectiveness of Logistic Regression in predicting heart disease using a dataset derived from multiple international databases. Employing a 5-fold cross-validation method, the research aimed to evaluate the model's accuracy, precision, recall, and F1-score. Results indicated that Logistic Regression performs robustly, with accuracy ranging from 80% to 88.29%, and high recall rates, highlighting its potential as a valuable tool in medical diagnostics. Despite some variability in precision, which may lead to higher false positive rates, the model's high recall is crucial in clinical settings where missing a diagnosis can have dire consequences. The research confirmed the applicability of Logistic Regression to binary classification problems in healthcare, aligning with existing literature that supports its use in similar contexts. The study contributes to the field by demonstrating the model's consistency and reliability across diverse data subsets, reinforcing the potential for machine learning applications in healthcare diagnostics. Future research should focus on integrating Logistic Regression with other models to improve accuracy and testing the model on more current, varied datasets to enhance its generalizability and effectiveness in real-world settings.
Segmentation and Feature Extraction for Malaria Detection in Blood Smears Rismayanti, Nurul
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.v2i1.138

Abstract

Malaria remains a critical global health challenge, particularly in tropical and subtropical regions. Early and accurate diagnosis is essential for effective treatment and control. Traditional methods of malaria diagnosis, such as microscopic examination of blood smears, are time-consuming and prone to human error. This study aims to develop an automated system for malaria detection using machine learning techniques, specifically a decision tree classifier. The dataset, sourced from the National Institutes of Health (NIH), comprises 27,558 blood smear images equally divided into Normal and Malaria classes. The preprocessing steps included segmentation using the Canny edge detector and feature extraction using Hu Moments, followed by data normalization to ensure a mean of 0 and variance of 1. The decision tree classifier was trained and evaluated using 5-fold cross-validation, yielding an average accuracy of 77.32%, precision of 77.31%, recall of 77.37%, and F1-Score of 77.48%. These results demonstrate the model's robustness and effectiveness in differentiating between malaria-infected and uninfected images. The study confirms the viability of using Hu Moments for feature extraction and highlights the decision tree classifier's suitability for this task. The proposed method has significant implications for automated malaria diagnosis, potentially improving diagnostic accuracy and efficiency in clinical settings. Future research should validate these findings on diverse datasets, explore advanced classification techniques, and integrate real-time image acquisition to enhance practical applicability. The integration of such automated systems in healthcare can revolutionize malaria diagnosis, especially in resource-limited settings.
Predicting the Conversion from Clinically Isolated Syndrome to Multiple Sclerosis in Mexican Mestizo Patients Using Gaussian Naive Bayes Classifier: A Prospective Cohort Study Yulinda, Nurul Kholifah
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.v2i1.139

Abstract

This study explores the application of the Gaussian Naive Bayes (GNB) classifier to predict the conversion from Clinically Isolated Syndrome (CIS) to Multiple Sclerosis (MS) among Mexican mestizo patients. Utilizing a dataset gathered from the National Institute of Neurology and Neurosurgery in Mexico City, which included patients diagnosed with CIS between 2006 and 2010, we employed a prospective cohort study design. Our approach involved preprocessing the data to handle missing values and scale normalization followed by splitting it into training and testing subsets. The GNB model's performance was assessed through a 5-fold cross-validation, focusing on accuracy, precision, recall, and F1-score. Results demonstrated the model's capability to predict MS conversion with reasonable precision, highlighted by a significant peak in performance metrics in the third fold of the validation. The study addresses a gap in predictive diagnostics for MS within a specific demographic group, providing valuable insights for early intervention strategies. Despite some limitations such as the model's sensitivity to data heterogeneity and the demographic specificity of the cohort, the findings underscore the potential for predictive models in clinical settings. Recommendations for future research include the use of more sophisticated algorithms and broader demographic studies to enhance predictive accuracy and generalizability.
Analysing Musculoskeletal Bone Fractures Using Decision Trees: A Deep Learning Approach with Canny Segmentation and Hu Moments Feature Extraction Riska, Riska
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.v2i1.140

Abstract

This study presents an in-depth analysis of the application of a Decision Tree classifier to detect bone fractures from X-ray images, leveraging the FracAtlas dataset containing 4,083 labelled images. The classifier underwent a rigorous evaluation using 5-fold cross-validation, focusing on metrics such as accuracy, precision, recall, and F1-score to ascertain its performance. Results varied across folds, with an accuracy range of 69.89% to 74.05%, precision between 72.27% and 73.75%, recall from 70.50% to 73.81%, and F1-scores of 71.52% to 73.31%. A graphical depiction of these metrics provided a visual comparison of performance consistency, while the confusion matrix offered a detailed account of the model’s predictive success and shortcomings. The research confirms the hypothesis that integrating Canny edge detection for segmentation and Hu Moments for feature extraction with a Decision Tree approach can facilitate fracture identification, positing the model as a supportive tool for radiologists. The study's findings contribute to the field of medical image analysis, suggesting that machine learning can be a valuable asset in clinical diagnostics. Recommendations for future research include the exploration of more complex algorithms, expansion of the dataset, and refinement of pre-processing techniques, to enhance the model's diagnostic precision further.
Machine Learning Approaches to Gastrointestinal Disease Diagnosis: An Experimental Study with Endoscopic Images Rismayanti, Nurul
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): 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.v1i1.147

Abstract

Automatic detection of gastrointestinal (GI) diseases using endoscopic images is a critical and emerging field of research with significant implications for healthcare. This study leverages the Kvasir dataset, available on Kaggle, to develop a machine learning model for disease detection and classification. The dataset, consisting of annotated images from the GI tract, was pre-processed using Canny edge detection for segmentation and Hu Moments for feature extraction. The images were divided into training (80%) and testing (20%) sets. A Random Forest Classifier was employed to classify three specific classes: dyed lifted polyps, dyed resection margins, and esophagitis. The performance of the classifier was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results showed moderate performance with accuracy ranging from 39.00% to 44.67%, precision from 40.68% to 47.34%, recall from 39.00% to 44.67%, and F1-scores from 38.09% to 45.07%. These findings indicate that while the Random Forest Classifier demonstrates potential, there is room for improvement in the model and pre-processing techniques. The study contributes to the field by providing a comprehensive evaluation of a machine learning approach for GI disease detection and highlights the need for further research using more advanced models and diverse datasets. Future research should focus on optimizing pre-processing methods, exploring convolutional neural networks, and expanding the dataset to improve classification performance and clinical applicability.
Detection and Classification of Bacterial Skin Infections Using K-Nearest Neighbors Algorithm Hayatou Oumarou
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): 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.v1i2.149

Abstract

Bacterial skin infections, including cellulitis and impetigo, pose significant health challenges requiring timely and accurate diagnosis for effective treatment. This research aims to develop an automated classification system for these infections using image processing and machine learning techniques. The study utilizes the Sobel method for image segmentation and Hu Moments for feature extraction. The classification is performed using the K-Nearest Neighbors (K-NN) algorithm with . The dataset, sourced from Kaggle, consists of imbalanced images of the two infection types. After pre-processing and feature extraction, the dataset is scaled to zero mean and unit variance. The model's performance is evaluated using cross-validation, yielding mean accuracy, precision, recall, F1-score, and ROC-AUC values of 65.95%, 65.18%, 65.95%, 63.06%, and 64.13%, respectively. Visualizations, including scatter plots, boxplots, histograms, correlation heatmaps, PCA, t-SNE, and UMAP, provide insights into the feature distributions and separability of classes. The results indicate that the combination of Sobel segmentation, Hu Moments, and K-NN can effectively classify bacterial skin infections. The study's contributions include demonstrating the applicability of these techniques to dermatological diagnostics and highlighting the potential for improved diagnostic accuracy and efficiency. However, the study acknowledges limitations such as data imbalance and variability in performance, suggesting the need for further research using advanced models like convolutional neural networks (CNNs) and enhanced data pre-processing techniques. These findings underscore the importance of machine learning in developing practical tools for clinical use, ultimately improving patient outcomes through early and accurate diagnosis.
Enhancing Alzheimer's Disease Diagnosis with K-NN: A Study on Pre-processed MRI Data Khasanah, Imroatul
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.v2i1.150

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, necessitating early and accurate diagnosis for effective intervention. This study evaluates the performance of the K-Nearest Neighbor (K-NN) algorithm on a pre-processed Alzheimer MRI dataset, focusing on the challenge of imbalanced classes. The dataset, sourced from Kaggle, comprises 6400 MRI images resized to 128x128 pixels and categorized into four classes: Non-Demented, Mild Demented, Moderate Demented, and Very Mild Demented. Pre-processing involved segmentation using the Canny edge detection method and feature extraction through Hu Moments. The dataset was split into training (80%) and testing (20%) sets, with features scaled to a mean of 0 and variance of 1. The K-NN algorithm was evaluated using cross-validation with five different k values, revealing moderate performance metrics: accuracy ranging from 45.86% to 50.47%, precision from 41.87% to 47.00%, recall from 45.86% to 50.47%, F1-score from 42.42% to 47.58%, and ROC AUC from 55.18% to 58.87%. The results highlight the significant impact of class imbalance on the algorithm's performance, particularly for the underrepresented Moderate Demented class. This study underscores the need for techniques to address class imbalance to enhance classification accuracy. Future research should explore advanced methods such as data augmentation, re-sampling, and ensemble learning, as well as the evaluation of other machine learning models. These findings contribute to the field of medical image analysis and have practical implications for improving diagnostic tools for Alzheimer's disease.

Page 2 of 5 | Total Record : 41

 1   2   3   4   5 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (2025): International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 2 (2024): International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 1 (2024): International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues