Smart healthcare disease diagnosis and patient management: Innovation, improvement and skill development

Abstract Data mining (DM) is an instrument of pattern detection and retrieval of knowledge from a large quantity of data. Many robust early detection services and other health-related technologies have developed from clinical and diagnostic evidence in both the DM and healthcare sectors. Artificial Intelligence (AI) is commonly used in the research and health care sectors. Classification or predictive analytics is a key part of AI in machine learning (ML). Present analyses of new predictive models founded on ML methods demonstrate promise in the area of scientific research. Healthcare professionals need accurate predictions of the outcomes of various illnesses that patients suffer from. In addition, timing is another significant aspect that affects clinical choices for precise predictions. In this regard, the authors have reviewed numerous publications in this area in terms of method, algorithms, and performance. This review paper summarized the documentation examined in accordance with approaches, styles, activities, and processes. The analyses and assessment techniques of the selected papers are discussed and an appraisal of the findings is presented to conclude the article. Present statistical models of healthcare remedies have been scientifically reviewed in this article. The uncertainty between statistical methods and ML has now been clarified. The study of related research reveals that the prediction of existing forecasting models differs even if the same dataset is used. Predictive models are also essential, and new approaches need to be improved.