Heart Disease Prediction using Machine Learning and Data Analytics Approach
IEEE BASE PAPER ABSTRACT:
In recent times, Machine Learning has played a significant role in the healthcare industry and amongst all of the major diseases, heart disease is one of the significant and most critical diseases to predict. There is a rapid increase in the number of cases each day. It has been observed that in every minute, 4 people between the age group of 30-50 get a stroke, so we are using machine learning algorithms to mitigate this problem. Kaggle used the heart disease dataset used for this project. This paper demonstrates the prediction of heart disease using multiple machine learning classification algorithms such as Naive Bayes, Random Forest, SVM etc., and compares their accuracy scores. Later on, Stacking Ensemble Learning Technique is used to boost our classification models’ performance.
OUR PROPOSED ABSTRACT:
The World Health Organization (WHO) estimates that 17.7 million people worldwide die suddenly each year as a result of cardiovascular illnesses. The people may benefit from the ability to foresee the complexity of their health at an early stage thanks to the heart disease prediction system. A doctor’s examination or a variety of medical tests, such as an ECG, a stress test, a heart MRI, etc., are the traditional methods for predicting heart disease. There is a vast quantity of hidden information in the health care data that is already available. Making wise decisions benefits from having access to this hidden information. For acceptable findings, computer-based data as well as advanced data mining techniques are used. Existing systems do a good job of accurately predicting the outcome, but more data attributes and the complexity of health parameters form the foundation for the development of novel strategies. In this proposed system we implement Heart Disease Prediction using Artificial Neural Network (ANN). The project “Heart Disease Prediction using Artificial Neural Network (ANN)” aims to develop a predictive model for the early detection of heart disease using ANNs. ANNs are powerful machine learning algorithms that can learn patterns and relationships in data, making them an ideal choice for predicting complex medical conditions like heart disease. The proposed system is implemented using Cleveland Heart Disease dataset available on UCI machine learning repository / Kaggle. This data is then pre-processed and used to train an ANN model using supervised learning techniques. The model is optimized to achieve high accuracy in predicting the likelihood of heart disease in patients. The trained model is then tested on a separate dataset to evaluate its performance and accuracy. Finally, the project aims to develop a user-friendly interface that allows doctors and healthcare professionals to input patient data and receive the predicted results. The proposed model has the potential to improve the accuracy and speed of heart disease diagnosis, enabling early intervention and better patient outcomes. This project can contribute to the development of AI-powered healthcare solutions and can have a significant impact on public health.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Artificial Neural Network (ANN).
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
Sanath Kapoor, Lekhraj Kasar, Ashutosh Mandole and Dr Jayant Mahajan, “Heart Disease Prediction using Machine Learning and Data Analytics Approach”, 7th International Conference on Computing in Engineering & Technology (ICCET 2022), IEEE Conference, 2022.
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