A study of Cardiovascular disease Risk factors using ML techniques

Worldwide, machine learning (ML) is applied in the healthcare industry. Machine learning approaches enable the preservation of cardiac illnesses and locomotor disorders in the medical data collection. Researchers now have a clear understanding of how to apply their diagnosis and course of treatment for a particular patient thanks to the disclosure of such important information. Medical professionals can identify diseases thanks to research using a variety of machine learning (ML) techniques to analyze vast volumes of complex healthcare data. In this paper, we used a 303-row, 76-property online UCI dataset for this investigation. It is crucial to evaluate the effectiveness of various strategies in order to assess about 14 of these 76 attributes. The isolation forest method makes advantage of the key characteristics of the data set.