Machine Learning Based Heart Disease Prediction System

Machine Learning Based Heart Disease Prediction System

₹5,500.00
Product Code: Python - Machine Learning
Availability: In Stock
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Product Description

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.

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 different machine learning methods that are 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 and data mining techniques. We proposed Support Vector, KNN, Decision Tree and Random Forest machine learning technique for heart disease prediction of significant features. Random forest classifier gives the high accuracy.  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 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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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.