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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal Medical Informatics Technology International Journal of Computer Technology and Science
Faruq Aziz
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Computer Science & IT Dentistry Engineering Medicine & Pharmacology Public Health

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Journal : journal medical informatics technology

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A Tripartite Machine Learning Approach for Accurate Prognosis of COVID-19 Patient Survival Faruq Aziz
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.13

Abstract

Accurate prognosis of COVID-19 patient survival is vital for healthcare decision-making. This research proposes a tripartite machine learning approach that integrates K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost for outcome prediction. Our hybrid model exploits the strengths of individual algorithms and combines their predictions using a weighted ensemble. Leveraging clinical data, KNN captures local patterns, SVM finds complex boundaries, and XGBoost enhances overall performance. Experimental results show exceptional precision (0.93), recall (0.93), and F1-score (0.93) for both classes, affirming accurate classification of "Alive" and "Died" cases. The achieved accuracy of 0.93 further demonstrates the reliability of the proposed approach. Our tripartite method holds the potential to enhance COVID-19 survival prediction, providing valuable insights for clinical practitioners and policymakers. This study contributes by seamlessly fusing KNN, SVM, and XGBoost models into a robust predictive tool, thereby aiding medical professionals in informed decision-making for patient care and resource allocation. The demonstrated success underscores the efficacy of a combined approach, highlighting its relevance in accurately predicting patient outcomes.
Efficient Skin Lesion Detection using YOLOv9 Network Faruq Aziz; Saputri, Daniati Uki Eka
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.30

Abstract

Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin lesion detection using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance in accurately identifying various skin lesion types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, and wart. We conducted comprehensive experiments using a curated dataset comprising 2721 training images, 288 validation images, and 145 test images. The model was trained and evaluated based on standard metrics such as Precision, Recall, and mean Average Precision (mAP). Our results indicate promising detection accuracy, with an overall Precision of 60.5%, Recall of 86.0%, and an mAP of 81.4%. Class-wise analysis reveals varying levels of performance across different disease classes, highlighting the model's proficiency in detecting common dermatological conditions such as acne and wart lesions. Furthermore, we provide insights into potential challenges and limitations, including dataset size and class imbalance, and discuss avenues for future research to address these issues. Our study contributes to the advancement of AI-driven solutions for dermatological diagnosis and underscores the efficacy of the YOLOv9 network in skin lesion detection
Co-Authors Alya Shafra Hewiz Daniati Uki Eka Saputri Dika Putri Metalica Dwiza Riana Jufriadif Na`am, Jufriadif Nurul Khasanah Saputri, Daniati Uki Eka Saputri Sri Hadianti Suryadi Putra Liawatimena Taopik Hidayat Teguh Herwanto