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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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Performance Analysis of the Decision Tree Classification Algorithm on the Pneumonia Dataset Ahmad Naswin; Adityo Permana Wibowo
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.v1i1.83

Abstract

The rapid advancements in machine learning have paved the way for innovative approaches in medical imaging diagnostics. In this context, this study explored the efficacy of the Decision Tree Classification Algorithm for distinguishing between normal and pneumonia-diagnosed X-ray images. We sourced our dataset from pediatric X-rays obtained from the Guangzhou Women and Children’s Medical Center. To enhance the classifier's performance, a methodical pre-processing strategy was adopted. This encompassed the application of the Canny segmentation technique, followed by feature extraction using humoments. The evaluation phase involved a 5-fold cross-validation, revealing a commendable average accuracy of 82.72%. These findings highlight not only the utility of Decision Trees in such specialized diagnostic tasks but also accentuate the pivotal role of systematic pre-processing in achieving optimal results. As medical diagnostics steadily move towards automation, this research provides valuable insights and benchmarks for future endeavors aiming to harness the power of machine learning in healthcare.
Effectiveness Evaluation of the RandomForest Algorithm in Classifying CancerLips Data Siti Khomsah; Edi Faizal
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.v1i1.84

Abstract

Lip cancer, though less commonly discussed, remains a significant concern in the realm of oncology. Early detection and diagnosis are paramount for improved patient outcomes. This research evaluated the effectiveness of the RandomForest algorithm in classifying the CancerLips dataset, a collection of lip images processed using the Canny segmentation method and described using Hu moments. Using a 5-fold cross-validation approach, the algorithm achieved an average accuracy of approximately 70.96%. The results highlight the potential of machine learning techniques, specifically RandomForest, in aiding lip cancer detection. However, the choice of preprocessing methods and feature extraction plays a crucial role in determining the outcome. The study underscores the need for further research, focusing on algorithm optimization and comparisons with other datasets or feature extraction methods, to enhance diagnostic precision in medical imaging.
Application of the K-Nearest Neighbors (KNN) Algorithm on the Brain Tumor Dataset Najwaini, Effan; Thomas Edyson Tarigan; Fajri Profesio Putra; Sulistyowati
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.v1i1.85

Abstract

Brain tumors pose significant challenges in the medical domain, necessitating advanced diagnostic techniques for early and accurate detection. This research paper presents a comprehensive study on the application of the K-Nearest Neighbors (KNN) algorithm to a dataset comprising brain tumor images. The methodology involved segmenting the images using the Canny method, extracting relevant features via Hu Moments, and subsequently employing the KNN algorithm for classification. Using a 5-fold cross-validation, the system consistently achieved an average accuracy of approximately 62%. These findings highlight the potential of traditional machine learning algorithms in medical imaging, providing valuable insights for both researchers and practitioners. While the results are promising, the study also underscores the importance of integrating such algorithms with other diagnostic methods for optimal results
Classification Optimization of Skin Cancer Using the Adaboost Algorithm Sumiyatun; Maulidinnawati, Andi
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.v1i1.86

Abstract

Early detection of melanoma skin cancer is crucial in improving prognosis and saving lives. This research aimed to optimize the classification of melanoma images using the Adaboost algorithm. Employing a dataset of 10,000 melanoma images, the study combined the Canny method for image segmentation, Hu Moments for feature extraction, and the Adaboost algorithm for classification. The 5-fold cross-validation results revealed an average accuracy of 61.52%. While the precision consistently surpassed recall, indicating the model's conservative nature in predicting positive cases. The outcomes align with previous research, emphasizing the challenges in melanoma classification. This study contributes to the domain by showcasing the potential and areas of improvement for machine learning in early melanoma detection. Future research is recommended to explore hybrid models and diversify data sources for enhanced robustness and generalizability.
Comparative Study on the Performance of the Bagging Algorithm in the Breast Cancer Dataset Fadhila Tangguh Admojo; Waluyo Poetro, Bagus Satrio
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.v1i1.87

Abstract

Breast cancer remains a predominant health concern globally. Early detection, powered by advancements in medical imaging and computational methods, plays a vital role in enhancing survival rates. This research delved into the application and performance of the Bagging algorithm on a Breast Cancer dataset that underwent image segmentation using the Canny method and feature extraction through Hu-Moments. The Bagging algorithm demonstrated moderately consistent performance across a 5-fold cross-validation, with average metrics of 56.9% accuracy, 58.3% precision, 57.7% recall, and 56.6% F-measure. While the results showcased the potential of the Bagging algorithm in classifying breast cancer data, there remains an avenue for further optimization and exploration of other ensemble or deep learning techniques. The findings contribute to the broader domain of machine learning in medical imaging and offer insights for future research directions and clinical diagnostic tool development.
Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset Azdy, Rezania Agramanisti; Syam, Rahmat Fuadi; Faizal, Edi; Sumiyatun, Sumiyatun
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.v1i2.96

Abstract

In this study, titled "Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset," we explore the effectiveness of the Bagging Meta-Estimator in diagnosing lung diseases, focusing on the challenges of imbalanced datasets. Utilizing a dataset segmented and characterized by Hu moments and encompassing categories of Normal, Bacterial Pneumonia, and Tuberculosis, the algorithm's performance was assessed through a 5-fold cross-validation. Results indicated moderate effectiveness with an average accuracy of 60.574%, precision of 60.749%, recall of 59.753%, and F1-Score of 59.416%, highlighting variable performance across folds. These findings suggest that while the Bagging Meta-Estimator has potential in medical imaging, further refinement is needed for consistent and reliable lung disease detection, especially in imbalanced datasets.
Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study Sinra, A.; Waluyo Poetro, Bagus Satrio; Angriani, Husni; Zein, Hamada; Musdar, Izmy Alwiah; Taruk, Medi
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.v1i2.97

Abstract

This study explores the efficacy of a Voting Classifier, combining Logistic Regression, Random Forest, and Gaussian Naive Bayes, in the classification of neurodegenerative diseases, focusing on Alzheimer's Disease (AD), Parkinson’s Disease (PD), and control groups. Utilizing a dataset pre-processed with Canny segmentation and Hu Moments feature extraction, the research aimed to address the challenges posed by imbalanced datasets in medical image classification. The classifier's performance was evaluated through a 5-fold cross-validation approach, with metrics including accuracy, precision, recall, and F1-Score. The results revealed a consistent recall rate of approximately 46% across all folds, indicating the model's effectiveness in identifying cases of neurodegenerative diseases. However, the precision and F1-Score were notably lower, averaging around 22% and 29%, respectively, underscoring the difficulties in achieving accurate classification in imbalanced datasets. The study contributes to the understanding of machine learning applications in medical diagnostics, specifically in the challenging context of neurodegenerative disease classification. It highlights the potential of using advanced image processing techniques combined with machine learning ensembles in enhancing diagnostic accuracy. However, it also draws attention to the inherent challenges in such approaches, particularly regarding precision in imbalanced datasets. Recommendations for future research include exploring data balancing techniques, alternative feature extraction methods, and different machine learning algorithms to improve the precision and overall performance. Additionally, applying the model to a broader and more diverse dataset could provide more generalizable and robust findings. This study is significant for researchers and practitioners in medical imaging and machine learning, offering insights into the complexities and potential of automated disease classification
Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Tarigan, Thomas Edyson; Susanti, Erma; Siami, M. Ikbal; Arfiani, Ika; Jiwa Permana, Agus Aan; Sunia Raharja, I Made
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.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes Rismayanti, Nurul; Naswin, Ahmad; Zaky, Umar; Zakariyah, Muhammad; Purnamasari, Dwi Amalia
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.v1i2.99

Abstract

This study, titled "Evaluating Thresholding-Based Segmentation and HuMoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes," investigates the application of image processing and machine learning techniques in the classification of Acute Lymphoblastic Leukemia (ALL). Utilizing a dataset of microscopic blood smear images, the research focuses on the efficacy of thresholding-based segmentation and Hu moment feature extraction in distinguishing between benign and malignant cases of ALL. Gaussian Naive Bayes, known for its simplicity and effectiveness, is employed as the classification algorithm. The study adopts a 5-fold cross-validation approach to evaluate the model's performance, with particular emphasis on metrics such as accuracy, precision, recall, and F1-score. Results indicate a high precision rate across all folds, averaging approximately 84.13%, while exhibiting variability in accuracy, recall, and F1-scores. These findings suggest that while the model is effective in identifying malignant cases, further refinements are necessary for improving overall accuracy and consistency. This research contributes to the field of medical image analysis by demonstrating the potential of combining simple yet efficient techniques for the automated diagnosis of hepatological diseases. It highlights the importance of integrating image processing with machine learning to enhance diagnostic accuracy in medical applications.
Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques Setiawan, Rudi; Kadir Parewe, Andi Maulidinnawati Abdul; Latipah, Asslia Johar; Puji Astuti, Nur Rochmah Dyah; Murdiyanto, Aris Wahyu; Putra, Fajri Profesio
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.v1i2.100

Abstract

This study investigates the efficacy of the Bagging-meta estimator in classifying CT kidney diseases, focusing on an imbalanced dataset processed through Sobel segmentation and Hu moment feature extraction. The research utilized a quantitative approach, applying the Bagging-meta estimator to a dataset comprising CT images classified into four categories: Normal, Cyst, Tumor, and Stone. These images were preprocessed using Sobel segmentation to highlight critical structures and Hu moment feature extraction for robust classification features. The study employed a 5-fold cross-validation method to evaluate the model's performance, assessing metrics such as accuracy, precision, recall, and F1-Score. The results indicated a significant variation in the model's performance across different folds, with accuracy ranging from 49.86% to 66.17%, precision between 51.86% and 65.93%, recall from 57.95% to 64.44%, and F1-Scores spanning 48.26% to 60.74%. These findings suggest that while the Bagging-meta estimator can achieve reasonable accuracy in classifying kidney diseases from CT images, its performance is affected by the imbalanced nature of the dataset. This study contributes to the understanding of the challenges and potential of machine learning in medical imaging, particularly in the context of imbalanced datasets. It highlights the need for specialized approaches to handle such datasets and underscores the importance of preprocessing techniques in enhancing model performance. Future research directions include exploring methods to address data imbalance, investigating alternative feature extraction techniques, and testing the model on diverse datasets to enhance its generalizability and reliability in clinical settings. This research offers valuable insights into the development of automated diagnostic tools in medical imaging and advances the field of computer-aided diagnosis in nephrology.

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