Machine Learning and Deep Learning Approaches for Healthcare Predictive Analytics

Healthcare prediction has been essential to life-saving measures in recent years. Intelligent technologies that evaluate complex data linkages and convert them into actionable insights for prediction purposes are rapidly emerging in the healthcare industry. Artificial intelligence (AI) is the main force behind this revolutionary process, with a focus on deep learning and machine learning systems. By utilizing a variety of data sources, including clinical data and medical imaging, these technologies are essential for both detecting and forecasting illnesses. When AI -driven predictive analytics are used in healthcare, they may simulate human perception and, in certain situations, even identify illnesses that may be invisible to the human eye. This results in significant therapeutic support. One cannot overestimate the importance of predictive analytics in healthcare as it directly affects how accurately diseases are predicted. While incorrect forecasts might put patients in danger, precise and timely forecasts have the power to save lives. Thus, the creation of trustworthy and effective techniques for predictive analysis in healthcare is essential. With an emphasis on healthcare prediction, this article aims to provide an extensive overview of state-of-the-art machine learning and deep learning techniques. Furthermore, the goal is to recognize and tackle the innate difficulties linked to implementing these strategies in the healthcare industry.