In the healthcare industry, there have recently been a number of problems with data processing and storage. Because fresh medical issues are emerging among the population, the quantity of data is growing every day. The healthcare industry can employ algorithms developed using machine learning (ML) in a variety of ways since ML-based techniques help doctors with diagnosis, prevention, treatment, and particularly during surgeries. Only a few industries are not taking advantage of self-learning computer networks and more IT (information technology) resources. On the other hand, the Internet of Things (IOT) is essential to the health care system and helps make it smarter. Information on patient diagnoses is taken from a large number of medical records. The provided data is handled by the typical scaling process. A resilient scalar is used to scale characteristics in a system like that. Principal component analysis (PCA) is used to minimize the dimension. The dimensions reduced data has been classified using the more reliable random forest classifier. Later, it is found that the classification results are more accurate.