Cardiovascular Diseases (CVD) is a group of dis- eases that affect a person’s heart and blood vessels. Compared to other diseases, this is one of the leading causes of mortality worldwide. Early detection is critical in many hearts related illnesses to reduce the number of deaths. Loss of life could arise from improperly analyzing the precise symptoms of risky diseases. A system that uses optimal algorithms to analyze human vital patterns and anticipate serious diseases is created. Predicting the probability of cardio diseases using human vital patterns is highlighted here. This project offers a prediction model to determine if a patient has a heart illness or not based on symptoms given in a web-form, as well as to raise awareness about heart disease and provide some helpful heart disease suggestions. Both supervised and unsupervised machine learning algorithms are used in this system. The web page is the main method of communication in this system. After entering the necessary information into the system, the system will notify the user whether he/she has a cardiac problem. Furthermore, if the data (blood pressure, heart rate) surpass the threshold limits, an emergency alert is sent to hospitals and ambulatory care facilities.

Keywords : Cardio, Supervised, Unsupervised.