Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning
By Rohit Bharti, A. Khamparia, Mohammad Shabaz, Gaurav Dhiman, Sagar Dhanraj Pande, Parneet Singh. Computational Intelligence and Neuroscience, 2021
509 citations recorded by the source corpus.
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.
Subfields: Cardiology and Cardiovascular Medicine, Health Information Management, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/31cf4c96c5dd4ac5a6bbb4ac7b6bab763651624a
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In Cardiology and Cardiovascular Medicine
In Health Information Management
Citations in the corpus
- Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, methodology): “Researchers have used multiple scenarios based on ML and DL models to predict conditions such as liver disease, heart disease, Alzheimer’s disease, and various types of cancers for which early detection is vital for treating [20–22].”
- Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, unknown): “[41, 134, 135, 42, 43, 44, 45] Heart disease Cleveland database https://archive.”