Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

By Senthilkumar Mohan, Chandrasegar Thirumalai, Gautam Srivastava. IEEE Access, 2019

1,389 citations recorded by the source corpus.

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).

Subfields: Cardiology and Cardiovascular Medicine, Health Information Management, Information Systems

View Paper: https://www.semanticscholar.org/paper/2bc3644ce4de7fce5812c1455e056649a47c1bbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Ahad Nasab
2. Nafiseh Ghaffar Nia
3. Amin Ul Haq
4. Erkan Kaplanoglu

In Health Information Management

1. M. Ahsan
2. Zahed Siddique
3. Ahad Nasab
4. Amna Eleyan
5. Mohammed Ali Moni
6. Parth Patel
7. Chintan M. Bhatt
8. Nafiseh Ghaffar Nia

In Information Systems

1. Ahad Nasab
2. Kawsar Ahmed
3. Mohammed Ali Moni
4. Julian M. W. Quinn
5. Md Mamun Ali
6. Nafiseh Ghaffar Nia
7. Francis Minhthang Bui
8. Amin Ul Haq

Citations in the corpus

• Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, methodology): “They used a hybrid random forest classifier and gained 88.7% accuracy [38].”
• Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, background): “(2019) [29] Effective Heart disease prediction Using hybrid Machine Learning techniques 214”
• Heart Disease Prediction Using Machine Learning Algorithms (2024, background): “In their study, S. Bagavathy et al. [27] investigated various data mining techniques to extract information from large datasets.”
• Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques (2021, neutral): “[32] with different combinations of features”
• Effective Heart Disease Prediction Using Machine Learning Techniques (2023, background): “Many factors, such as diabetes, high blood pressure, high cholesterol, and abnormal pulse rate, need to be considered when predicting heart disease [11].”
• Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison (2021, methodology): “Mohan et al., in 2019 introduced a heart disease prediction model using hybrid machine learning techniques [15].”
• Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare (2020, methodology): “[27] designed a HD prediction method by using hybrid machine learning techniques.”