Heart disease prediction and recommendation system using machine learning | |
| Author | Rao, Vemulapalli Sai Rama Koteswara |
| Call Number | AIT RSPR no.IM-21-07 |
| Subject(s) | Heart--Diseases--Data processing Machine learning Recommender systems (Information filtering) |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | Heart disease prediction is an important application of machine learning in the healthcare industry. Detecting heart disease in the early stage can prevent the chances of occurrence of heart disease. In this study, use of data mining techniques and machine learning algorithms in predicting heart disease have been analysed and implemented. The objective of this research study is to predict the occurrence of heart disease and give recommendations to the user accordingly for the factors that are needed to be taken care of. This paper proposes an architecture for predicting heart disease using those techniques and after predicting the occurrence of heart disease, the system additionally gives users the features that are contributing towards the disease along with some recommendations based on their data. This is carried out by using different data mining techniques on the data and implementing Random Forest, Logistic regression and SVM algorithms for building the model and using Shap. Shap is a library in python used for the interpretation of the machine learning output. Based on the Output from the Shap Interpretation the system gives recommendations to the user for the features contributing towards disease. These recommendations are given to the user based on the data that is collected from trusted healthcare publications which are openly available. Out of the three algorithms Random Forest performed the best with an accuracy of 83.5%. Therefore, the system is built on Random Forest Model. The data is collected from the UCI machine learning repository. |
| Year | 2021 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Vatcharapon Esichaikul |
| Examination Committee(s) | Chutiporn Anutariya;Chaklam Silpasuwanchai |
| Scholarship Donor(s) | AIT Fellowship |
| Degree | Research studies project report (M. Eng.) - Asian Institute of Technology, 2021 |