December 2024 in “International Journal of experimental research and review” In this study, the integration of obesity-related features and machine learning techniques significantly enhanced cardiovascular disease detection, with the XGBoost classifier achieving a 74% accuracy rate and improved metrics compared to other models.
79 citations
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July 2022 in “Sensors” In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.
May 2026 in “International Journal of Scientific Research in Science and Technology” This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.
February 2024 in “Frontiers in physics” This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.
2 citations
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January 2024 in “IEEE Access” This study introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.
