A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms
TLDR
The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently and can easily identify and classify people with heart disease from healthy people.Abstract:
Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.read more
Citations
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Book ChapterDOI
Statistical Pattern Recognition
TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
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Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
TL;DR: The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease and it achieved good accuracy as compared to previously proposed methods.
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Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques
Pronab Ghosh,Sami Azam,Mirjam Jonkman,Asif Karim,F. M. Javed Mehedi Shamrat,Eva Ignatious,Shahana Shultana,Abhijith Reddy Beeravolu,Friso De Boer +8 more
TL;DR: In this article, the authors proposed a model that incorporates different methods to achieve effective prediction of heart disease, which used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model.
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An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier
TL;DR: An IoT framework to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN) and demonstrates that the proposed MDCNN based heart disease prediction system performs better than other methods.
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HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System
TL;DR: An effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and eliminate the outliers, a hybrid Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) to balance the training data distribution and XGBoost to predict heart disease.
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