Heart Disease Prediction Using Data Mining Techniques

Built an algorithm using the multilayer perceptron in machine learning that uses 14 medical parameters such as age, sex, blood pressure, cholesterol, and obesity for effective heart disease prediction. Took an existing data set, cleaned, and manipulated through python, created a UI using HTML and CSS

The Above Picture Shows the architecture of our System

As new technology emerges, we wish to provide a solution to the hospitals which is most economical and which consumes minimal resources and saves a lot of processing time. The aim of the system is to predict the diagnosis of a heart related disease in a person. The system uses the relevant details of an individual patient such as age, gender, hereditary details, frequency of exercise etc. by exploiting the historical data which is generated as a part of Content Management Systems in hospitals or medical institutes using various data mining techniques.

According to the most recent estimates from United States, cardiovascular disease (CVD) death rates have declined but the disease burden still remains substantially high.we discuss the common use of an individual’s age in prediction of CVD incidence using different risk scores, examine whether age as a risk factor can be modified or not, discuss the methods used to evaluate long- and short-term CVD risk, appropriate communication of an individual’s risk based on their age group and CVD risk, and conclude by discussing the influence of age on cardiac and vascular risk factors.

The Above Figure Sates the Gender differences in coronary heart disease and the below Figure says about the trends in the total annual number of deaths caused by cardiovascular disease according to gender

DataSets used :

1.Cleavland Dataset

2.Hungarian Dataset

3.Long Beach Dataset

Estimation Paremeters: Age , Sex , Chest Pain, Fasting Blood Sugar , Chol , Diabeties ,

RestECG, Exang, Slope, CA , Thal , Trestbpa,Thalch,Old peak ST