Worldwide, machine learning (ML) is applied in the healthcare industry. In the medical data set, ML techniques aid in the prevention of cardiac conditions and motor impairments. Finding such crucial information gives researchers important new understanding on how to apply their diagnosis and treatment for a specific patient. To help medical professionals forecast diseases, researchers analyze vast volumes of complex healthcare data using a variety of Machine Learning techniques. We are using an open UCI dat aset with 303 rows and 76 attributes for this study. Of these 76 qualities, about 14 are chosen for testing, which is required to verify how well various approaches work. The isolation forest method standardizes the data for increased accuracy by utilizing the most important attributes and metrics from the data collection.