Machine Learning Based Heart Disease Prediction System

Aim:

      To apply machine learning techniques result in improving the accuracy in the prediction of cardiovascular disease.

Abstract:

       In human life, healthcare is an unavoidable and important task to be done. Cardiovascular Diseases are a group of diseases that affects heart and blood vessels. The earlier methods of estimating the uncertainty levels of cardiovascular diseases helped in taking decisions to reduce the risk in high-risk patients. This project proposes a prediction model to predict whether a person has a heart disease or not and to provide awareness or diagnosis on the risk to the patient.The prediction model is projected with mixtures of various options and a number of other classification techniques. This is done by comparing the accuracies of different algorithms to the separate results of SVM, KNN, Decision Tree and Random Forest and uses the algorithm with high accuracy for prediction. Our goal is to enhance the performance of the model by removing unnecessary and insignificant attributes from the dataset and only collecting those that are most informative and useful for the classification task. Thus the main focus of the system is to make use data analytics to predict the presence of the disease and level of disease among patients.

 Existing System:

       Over the past decades, heart disease is a common and dangerous disease caused by fat suppression. This disease occurs due to overpressure in the human body. In traditional method doctors may make some mistakes in found a disease, but now days Machine learning play a great roll in prediction. We can predict cardiac disease using a variety of parameters in the dataset. The obtained results are compared with the results of existing models within the same domain and found to be improved. The data of heart disease patients collected from the UCI laboratory is used to discover patterns with Random Forest and Decision Tree. To make this system user friendly, so we move to the next update.

Problem Definition:

         The main problem addressed in this research is the prediction of heart disease in patients. Traditional diagnostic methods may have limitations in terms of accuracy and efficiency. This research aims to overcome these limitations by employing machine learning algorithms to analyze patient data and develop predictive models for heart disease.

Proposed System

      In previous studies, they have discussed predicting the significant features of heart disease prediction by using different machine learning and data mining techniques. We proposed Support Vector, KNN, Decision Tree andRandom Forest machine learning technique for heart disease prediction of significant features.ML process starts from a pre-processing data phase followed by feature selection based on data cleaning, classification of modeling, performance evaluation, and the results with improved accuracy. We create a web application using Flask.First client should be register themselves on the registration pageinweb application.Once the user logins into the system he gets all the access and user gives input to predict a heart disease.

Advantages:

         Early Detection: Machine learning algorithms have the potential to identify patterns and relationships in complex patient data, enabling early detection of heart disease before symptoms manifest. Improved Accuracy: By leveraging a wide range of patient data and applying advanced machine learning algorithms, the accuracy of heart disease prediction can be improved compared to traditional diagnostic methods.  

Applications: 

• Heart disease prediction may widely used in medical field. Doctors can able to detect more number of Heart diseases in a single day.

• It decreases the waiting time of Patients.