The field of medical diagnosis and prognosis is experiencing significant advancements with the integration of machine learning techniques. Researchers are exploring various machine learning models, including neural networks, deep learning, and hybrid approaches, to improve the accuracy and efficiency of diagnosis and prognosis in different medical applications. These innovations have the potential to revolutionize traditional diagnostic methodologies and provide personalized treatment strategies. Notably, studies have demonstrated the effectiveness of machine learning algorithms in predicting student academic performance, detecting diabetes, and diagnosing oral cancer. The use of dimensionality reduction techniques and feature selection methods is also being investigated to enhance model performance. Noteworthy papers include:

• Improved Allergy Wheal Detection for the Skin Prick Automated Test Device, which presents a custom-made approach for detecting and delineating wheals using a neural network component and an algorithmic approach.
• Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models, which proposes a hybrid strategy combining machine learning models with pretrained deep learning models to improve classification performance and reduce computational costs.