Predicting the Presence of Heart Diseases using Comparative Data Mining and Machine Learning Algorithms

Ananey-Obiri, Daniel; Sarku, Enoch

doi:10.5120/ijca2020920034

Cited by 31 publications

(23 citation statements)

References 8 publications

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“…In the same way, Ref. [ 18 ] presents a machine learning-based classification model for CVD prediction using the Cleveland dataset. For training, the feature reduction technique is used to select the 4 highly contributing attributes out of 13 attributes.…”

Section: Related Workmentioning

confidence: 99%

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

et al. 2022

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Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets.

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Section: Related Workmentioning

confidence: 99%

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

et al. 2022

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“…Several heart diagnosis studies in the state of the art [3,[6][7][8][9][10][11][12][13][14] have been done extraordinary works that contributed by providing different prediction approaches. These studies could be categorized based on the targeted prediction such as Heart Failure (HF) prediction [7], mortality or hospitalization prediction of the HF patient [3,6], and EHD prediction [8][9][10][11][12][13][14].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms and Data Mining Techniques for Predicting the Existence of Heart Disease

Alotaibi¹,

Alzahrani²

2022

IJACSA

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Heart diseases are considered one of the leading causes of death globally over the world. They are difficult to be predicted by a specialist physician as it is not an easy task which requires greater knowledge and expertise for prediction. With the variety of machine learning and deep learning algorithms, there exist many recent studies in the state of the art that have been done remarkable and practical works for predicting the presence of heart diseases. However, some of these works were affected by various drawbacks. Hence, this work aims to compare and analyze different classifiers, pre-processing, and dimensionality reduction techniques (feature selection and feature extraction) and study their effect on the prediction of heart diseases existence. Therefore, based on the resulting performance of several conducted experiments on the well-known Cleveland heart disease dataset, the findings of this study are: 1) the most significant subset of features to predict the existence of heart diseases are PES, EIA, CPT, MHR, THA, VCA, and OPK, 2) Naïve Bayes classifier gave the best performance prediction, and 3) Chi-squared feature selection was the data mining technique that reduced the number of features while maintained the same improved performance for predicting the presence of heart disease.

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“…They achieved a maximum increase in the accuracy of a weak classifier of 7.26% based on ensemble algorithm, and produced an accuracy of 85.48% using majority vote with NB, BN, RF, and MLP classifiers using an attribute set of nine attributes. D. Ananey-Obiri et al [20] developed three classification models, namely, LR, DT, and Gaussian naïve Bayes (GNB), for heart disease prediction based on the Cleveland dataset. Feature reduction was performed using single value decomposition, which reduced the features from 13 to 4.…”

Section: Related Workmentioning

confidence: 99%

“…During pre-processing, most researchers [18,19,21,22,26,[29][30][31][32] replaced the missing values, either by using the mean value or the majority mark of that attribute, to make sure the dataset was comprehensive. In some works [20,24,25,27], the missing valued instances were removed. Feature selection is a challenging task due to the large exploration space.…”

Section: Related Workmentioning

confidence: 99%

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

et al. 2021

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Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.

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Predicting the Presence of Heart Diseases using Comparative Data Mining and Machine Learning Algorithms

Cited by 31 publications

References 8 publications

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

Comparative Analysis of Machine Learning Algorithms and Data Mining Techniques for Predicting the Existence of Heart Disease

Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators

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