Prediction of Heart Disease Using Random Forest and Feature Subset Selection

Jabbar, M. A.; Deekshatulu, B. L.; Chandra, Priti

doi:10.1007/978-3-319-28031-8_16

Cited by 69 publications

(47 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Das et al [19] proposed an ANN ensemble-based predictive model that diagnoses the heart disease and used statistical analysis system enterprise miner 5.2 with the classification system and achieved 89.01% accuracy, 80.09% sensitivity, and 95.91% specificity. Jabbar et al [20] designed a diagnostic system for heart disease and used machine learning classifier multilayer perceptron ANN-driven back propagation learning algorithm and feature selection algorithm. e proposed system gives excellent performance in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Haq

Memon

et al. 2018

Mobile Information Systems

425

182

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Haq

Memon

et al. 2018

Mobile Information Systems

425

182

View full text Add to dashboard Cite

show abstract

“…20 ANN is an important tool for data mining of medical records for classification and prediction purposes. 22 In a large number of previous studies, neural network was used for classifying such diseases as dengue fever, [23][24][25] chest or heart diseases, 26,27 West Nile virus diseases, 28 tuberculosis, 29,30 gestational diabetes mellitus, 31 swine flu, 32 and pancreatic cancer. 33 These studies had helped in diagnosis and case management of epidemic victims.…”

Section: The Applications Of Ann In Epidemiologymentioning

confidence: 99%

A Review of Epidemic Forecasting Using Artificial Neural Networks

Philemon

Ismail

Jayeola

2019

Int J Epidemiol Res

View full text Add to dashboard Cite

Background and aims: Since accurate forecasts help inform decisions for preventive health-care intervention and epidemic control, this goal can only be achieved by making use of appropriate techniques and methodologies. As much as forecast precision is important, methods and model selection procedures are critical to forecast precision. This study aimed at providing an overview of the selection of the right artificial neural network (ANN) methodology for the epidemic forecasts. It is necessary for forecasters to apply the right tools for the epidemic forecasts with high precision. Methods: It involved sampling and survey of epidemic forecasts based on ANN. A comparison of performance using ANN forecast and other methods was reviewed. Hybrids of a neural network with other classical methods or meta-heuristics that improved performance of epidemic forecasts were analysed. Results: Implementing hybrid ANN using data transformation techniques based on improved algorithms, combining forecast models, and using technological platforms enhance the learning and generalization of ANN in forecasting epidemics. Conclusion: The selection of forecasting tool is critical to the precision of epidemic forecast; hence, a working guide for the choice of appropriate tools will help reduce inconsistency and imprecision in forecasting epidemic size in populations. ANN hybrids that combined other algorithms and models, data transformation and technology should be used for an epidemic forecast.

show abstract

“…Moreover, as we mentioned before, the Bayesian classifier is not as sensitive as decision tree classifier. Jabbar et al (2013) have proposed a classification model based on neural network; this model used a neural network to construct the classifier; it may be very useful in reducing the time consumed, but the accuracy may be not very satisfactory, and the model also used principle component analysis for preprocessing and to reduce number of attributes. Moreover as we mentioned before, neural network has a bad quality to deal with the non-numeric data and low learning rate.…”

Section: Classification Modelmentioning

confidence: 99%

Enhanced decision tree algorithm using genetic algorithm for heart disease prediction

Kumar

Sahoo

2018

IJBRA

View full text Add to dashboard Cite

In today's present scenario, heart disease has greater impact on our lives and identified fatal due to its high mortality rate. The diagnosis of heart disease is more challenging due to its vulnerability. Gone to limitation of previous work of literature survey, enhanced decision tree algorithm is introduced and applied on University of California, Irvine data sets. In order to predict heart disease, enhanced decision tree algorithm generates the decision rules which are later optimised by genetic algorithm. By then we examine the methods and operators of the algorithm. Finally our proposed algorithm is compared with decision tree (C4.5) and support vector machine algorithm, the proposed algorithm shows high accuracy, and its simplicity makes ideal for pattern recognition applications.

show abstract

Prediction of Heart Disease Using Random Forest and Feature Subset Selection

Cited by 69 publications

References 3 publications

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

A Review of Epidemic Forecasting Using Artificial Neural Networks

Enhanced decision tree algorithm using genetic algorithm for heart disease prediction

Contact Info

Product

Resources

About