Performance Evaluation of Distributed Machine Learning for Cardiovascular Disease Prediction in Spark
Product Description
Aim:
To apply machine learning techniques result in improving the accuracy in the prediction of cardiovascular disease.
Introduction:
Healthcare means the maintenance or advancement of health through the prevention and diagnosis of people. Nowadays, healthcare is increasing day by day due to lifestyle and hereditary. Cardiovascular disease has become the deadliest enemy. A person with cardiovascular disease cannot be cured simply. So, diagnosing patients at the correct time is the toughest work in the medical industry and needs to be diagnosed at initial stages to reduce the risk on the patient in the future. Every human body possesses different numbers for blood pressure, cholesterol, and pulse rate. But the normal values would be, blood pressure is 120/80, cholesterol is 200 mg/dl and pulse rate is 72. So combining these machine learning algorithms with medical data sources is useful. This paper suggests Pyspark MLlib that is useful for forecasting the uncertainty levels of cardiovascular disease for a person depending on the collected attributes.
Proposed System :
In previous studies, they have discussed predicting the significant features of heart disease prediction by using different machine learning. We proposed Pyspark machine learning Library (MLlib) 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. And also we get live data from the user with the help of Xampp server. That live data will be added to the existing dataset. We have an admin login, here we can able to add live data to the dataset. First client user should be register themselves on the registration page in web application. Once the user logins into the system he gets all the access and user gives input to predict a heart disease.
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Mini Projects - Hardware includes
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The Delivery time for Hardware Mini projects is 7-8 working days.