Evolution of Machine learning and AI in Disease Prediction Models

With the promise of advancing disease diagnosis, treatment, and prevention, machine learning and AI have produced intriguing new breakthroughs in the field of disease prediction. The requirement for open and understandable models as well as the ethical issues surrounding the use of personal health data are hurdles that must be overcome. It will be crucial to strike a balance between the potential advantages of these models and the need for privacy and ethical issues as the area develops. [1].Another issue is inefficiency, which results in low productivity. [2]. The field of healthcare has seen a tremendous transformation in recent years, with the increasing use of machine learning and artificial intelligence (AI) to develop disease prediction models. Disease prediction models using machine learning and AI algorithms have shown great promise in accurately predicting the likelihood of developing a disease based on various risk factors. These models have been developed using a range of techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.