Hybrid Model for Heart Disease Prediction Using Random Forest and Logistic Regression

The data mining (DM) is a process that deals with mining of valuable information from the rough data. The method of prediction analysis (PA) is implemented for predicting the future possibilities on the basis of current information. This research work is planned on the basis of predicting the heart disease. The coronary disorder can be forecasted in different phases in which pre-processing is done, attributes are extracted and classification is performed. The hybrid method is introduced on the basis of RF and LR.The Random Forest classification is adopted to extract the attributes and the classification process is carried out using logistic regression. The analysis of performance of introduced system is done with regard to accuracy, precision and recall. It is indicated that the introduced system will be provided accuracy approximately above 90% while predicting the heart disease.

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A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1831-1838 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1831

Author(s):

Tamilarasi Suresh ◽

Tsehay Admassu Assegie ◽

Subhashni Rajkumar ◽

Napa Komal Kumar

Keyword(s):

Support Vector Machine ◽

Heart Disease ◽

Random Forest ◽

Hybrid Model ◽

Regularization Parameter ◽

Hybrid Approach ◽

Experimental Result ◽

Medical Decision ◽

Support Vector ◽

Disease Prediction

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

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Human Heart Disease Prediction System Using Random Forest Technique

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i7.634640 ◽

2018 ◽

Vol 6 (7) ◽

pp. 634-640

Author(s):

H. Kaur ◽

D. Gupta

Keyword(s):

Heart Disease ◽

Random Forest ◽

Human Heart ◽

Disease Prediction ◽

Prediction System ◽

Human Heart Disease

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Bagging Technique to Reduce Misclassification in Coronary Heart Disease Prediction Based on Random Forest

Journal of Physics Conference Series ◽

10.1088/1742-6596/1477/3/032009 ◽

2020 ◽

Vol 1477 ◽

pp. 032009

Author(s):

A Saifudin ◽

U U Nabillah ◽

Yulianti ◽

T Desyani

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Random Forest ◽

Disease Prediction

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Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms

10.1109/iceem52022.2021.9480625 ◽

2021 ◽

Author(s):

Mohamed G. El-Shafiey ◽

Ahmed Hagag ◽

El-Sayed A. El-Dahshan ◽

Manal A. Ismail

Keyword(s):

Genetic Algorithms ◽

Heart Disease ◽

Random Forest ◽

Prediction Method ◽

Disease Prediction

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Heart Disease Prediction using Machine Learning Techniques

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst2183218 ◽

2021 ◽

pp. 42-47

Author(s):

Ramesh Ponnala ◽

K. Sai Sowjanya

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Linear Model ◽

Machine Learning Techniques ◽

Disease Prediction ◽

Huge Amount ◽

Healthcare Enterprise ◽

Learning Techniques ◽

Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

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Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3473.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 127-133

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Heart Disease ◽

Heart Diseases ◽

Experimental Results ◽

Disease Prediction ◽

Feature Importance ◽

The World ◽

Feature Scaling ◽

Logistic Regression Classifier

In today’s modern world, the world population is affected with some kind of heart diseases. With the vast knowledge and advancement in applications, the analysis and the identification of the heart disease still remain as a challenging issue. Due to the lack of awareness in the availability of patient symptoms, the prediction of heart disease is a questionable task. The World Health Organization has released that 33% of population were died due to the attack of heart diseases. With this background, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for analyzing and the prediction of heart disease by integrating the ensembling methods. The prediction of heart disease classes are achieved in four ways. Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier. Secondly, the highly importance features of each of the ensembling methods is filtered from the dataset and it is fitted to logistic regression classifier to analyze the performance. Thirdly, the same extracted important features of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. Fourth, the Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that before applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.04, MAE of 0.07, R2 Score of 92%, EVS of 0.86 and MSLE of 0.16 as compared to other ensembling methods. Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%, EVS of 0.93 and MSLE of 0.18 as compared to other ensembling methods.

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Heart Disease Prediction Using Decision Tree and Random Forest Classification Techniques

Applications of Big Data in Large- and Small-Scale Systems - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-6673-2.ch015 ◽

2021 ◽

pp. 234-259

Author(s):

Nitika Kapoor ◽

Parminder Singh

Keyword(s):

Feature Extraction ◽

Heart Disease ◽

Random Forest ◽

Decision Tree ◽

Random Forest Classifier ◽

Disease Prediction ◽

Decision Tree Classifier ◽

Hybrid Classifier ◽

Forest Classification ◽

Tree Classifier

Data mining is the approach which can extract useful information from the data. The prediction analysis is the approach which can predict future possibilities based on the current information. The authors propose a hybrid classifier to carry out the heart disease prediction. The hybrid classifier is combination of random forest and decision tree classifier. Moreover, the heart disease prediction technique has three steps, which are data pre-processing, feature extraction, and classification. In this research, random forest classifier is applied for the feature extraction and decision tree classifier is applied for the generation of prediction results. However, random forest classifier will extract the information and decision tree will generate final classifier result. The authors show the results of proposed model using the Python platform. Moreover, the results are compared with support vector machine (SVM) and k-nearest neighbour classifier (KNN).

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