Abstract
In our hectic lives, we usually do not have enough time to check our health
on a daily basis, and as a result, we disregard our health problems. The smart health
prediction system presented in this research uses a new method that could aid us in
taking care of ourselves. We can use the symptoms of our health problems as input to
our system to help us predict the condition, and then we can contact a medical
professional when necessary. The goal of this study is to use data mining techniques to
forecast cardiac disease. Due to its ability to effectively forecast outcomes and store
vast amounts of data, data mining is increasingly popular nowadays. Here, we examine
the information and display each aspect of the dataset. We display the male-to-female
patient ratio, the type of cardiovascular disease, type of chest discomfort, and
maximum and minimum patient ages. Then, we employ a variety of machine learning
approaches, including the Decision Tree Algorithm, Random Forest, Support Vector
Machine, Logistic Regression, KNN, and others, to forecast the disease. The majority
of the models offer us accuracy rates of over 85%. Additionally, it examines the
matrix's recall and precision. Therefore, we can infer that it provides us with a positive
outcome that will enable us to take the required precautions and lower the rate of
mortality associated with a heart disease or heart attack.