Heart Disease Classification Based on Hybrid Ensemble Stacking Technique

Sheikh, Ahmed El; Keshk, Arabi

doi:10.21608/ijci.2021.207732

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…A quantitative study using the ensemble model in conjunction with brute force as a technique for selecting features to classify heart diseases resulted in a remarkable accuracy rate of 97.8%. The suggested stacking model has been demonstrated to be efficient and outperforms existing techniques in the categorization of cardiac disorders [ 27 ].…”

Section: Clinical Applicationsmentioning

confidence: 99%

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Advances in the Applications of Bioinformatics and Chemoinformatics

Raslan

Shehata

et al. 2023

Pharmaceuticals

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Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as “in silico techniques”, in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer’s disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity—all the previously mentioned—were presented in the current review.

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Section: Clinical Applicationsmentioning

confidence: 99%

“…More active than tetracycline and less bacterial-resistant. [27] Bioinformatics Disease Classification…”

Section: Referencementioning

confidence: 99%

Advances in the Applications of Bioinformatics and Chemoinformatics

Raslan

Shehata

et al. 2023

Pharmaceuticals

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“…Stacking often accounts for a variety of weak learners [18]. Stacking architecture improves the accuracy of classification over a single classifier as it uses various ways to solve classification problems [19]. Stacking is a learning strategy that uses a meta-classifier to integrate the results of numerous basic classifiers learnt on the same dataset.…”

Section: Ensemble Stackingmentioning

confidence: 99%

Performance Comparison between Meta-classifier Algorithms for Heart Disease Classification

Zaini¹,

Awang²

2022

IJACSA

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The rise in heart disease among the general population is alarming. This is because cardiovascular disease is the leading cause of death, and several studies have been conducted to assist cardiologists in identifying the primary cause of heart disease. The classification accuracy of single classifiers utilised in most recent studies to predict heart disease is quite low. The accuracy of classification can be enhanced by integrating the output of multiple classifiers in an ensemble technique. Even though they can deliver the best classification accuracy, the existing ensemble approaches that integrate all classifiers are quite resource-intensive. This study thus proposes a stacking ensemble that selects the optimal subset of classifiers to produce meta-classifiers. In addition, the research compares the effectiveness of several meta-classifiers to further enhance classification. There are ten types of algorithms, including logistic regression (LR), support vector classifier (SVC), random forest (RF), extra tree classifier (ETC), naïve bayes (NB), extreme gradient boosting (XGB), decision tree (DT), k-nearest neighbor (KNN), multilayer perceptron (MLP), and stochastic gradient descent (SGD) are used as a base classifier. The construction of the meta-classifier utilised three different algorithms consisting of LR, MLP, and SVC. The prediction results from the base classifier are then used as input for the stacking ensemble. The study demonstrates that using a stacking ensemble performs better than any other single algorithm in the base classifier. The meta-classifier of logistic regression yielded 90.16% results which is better than any base classifiers. In conclusion, we could assume that the ensemble stacking approach can be considered an additional means of achieving better accuracy and has improved the performance of the classification.

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“…Djerioui et al (2020) projected the HD model for the identification of heart disease based on LSTM technique and is compared with the MLP, where LSTM achieved a high accuracy rate of 89.1% Shorewala (2021). Medina-Quero et al (2018) worked on the Cardio Vascular Disease (CVD) identification using Fuzzy Logic and RNN (El Sheikh et al, 2021;Haq et al, 2019;Kim et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A 2-Tier Stacking Ensemble Classifier for Disease Classification

Varshini,

Uthra

2023

Journal of Computer Science

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Diarrhea, dysentery, and dehydration are top of the list of high mortality-causing diseases in children under the age group of five. Despite the tremendous growth of machine learning in the field of medical research, still, some areas remain untouched, especially, the pediatric department. Since medical data is used in the proposed work, even a slight increase in the accuracy of the model will be of great importance. In this study, we propose a 2-tier stacking ensemble method for disease identification. Initially, the data is pre-processed and sent to train the tier-1 machine learning models. Based on the majority voting the metadata is selected and sent to the next tier and finally, at the meta-classifier level disease classification happens. Performance metrics like the accuracy, precision, recall, F1-score and mean absolute error of the individual machine learning algorithms were analyzed and were used to compare with the proposed stacking ensemble method. The results proved that the 2-tier stacking model proposed in the work shows an accuracy of 95.41%, a precision of 94.47%, a recall value of 92.78%, and an F1-score of 93.50%. The proposed model achieved a high accuracy value when compared to the other machine learning models.

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Heart Disease Classification Based on Hybrid Ensemble Stacking Technique

Cited by 5 publications

References 19 publications

Advances in the Applications of Bioinformatics and Chemoinformatics

Advances in the Applications of Bioinformatics and Chemoinformatics

Performance Comparison between Meta-classifier Algorithms for Heart Disease Classification

A 2-Tier Stacking Ensemble Classifier for Disease Classification

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