Machine Learning Based Heart Disease Prediction Model with GUI Interface

One of the top causes of death around the globe is a heart attack. According to current statistics, one person dies from heart disease every minute, making it one of the primary problems in everyday modern life. The ability to predict the onset of illness at an early stage is extremely difficult nowadays. When used in the healthcare industry, machine learning has the potential to accurately and quickly diagnose diseases. The circumstances under which heart disease may arise are estimated in this study. Medical parameters are characteristics of the datasets utilized. The datasets are analyzed using the Random Forest Algorithm, a machine learning algorithm, in Python. This method makes use of historical patient data from the past to forecast future ones at an early stage, saving lives. In this study, a trustworthy system for predicting heart disease is put into place utilizing a powerful machine learning algorithm called the Random Forest method. This reads a CSV file containing patient record data. After gaining access to the dataset, the procedure is carried out, and a useful heart attack level is generated. The suggested system's benefits include High success rates are attained, along with excellent performance and accuracy rates, flexibility, and adaptability.