An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

Rahim, Aqsa; Rasheed, Yawar; Azam, Farooque; Anwar, Muhammad Waseem; Rahim, Muhammad Abdul; Muzaffar, Abdul Wahab

doi:10.1109/access.2021.3098688

Cited by 96 publications

(38 citation statements)

References 41 publications

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“…The test results showed that MLDS has proven to be able to efficiently predict the risk of cardiovascular disease compared to five other different models. In the same context, Rahim et al [11] suggested a Machine Learning-based Cardiovascular Disease Diagnosis framework (MaLCaDD) to improve the prediction accuracy of cardiovascular diseases. Rahim et al stated that increasing the accuracy of the prediction using various feature selection and classification methods has taken the most attention of the researchers.…”

Section: Related Workmentioning

confidence: 99%

Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach

Abdulsalam¹,

Meshoul²,

Shaiba³

2023

Intelligent Automation &Amp; Soft Computing

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Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed model is a bagging ensemble learning model where a quantum support vector classifier was used as a base classifier. Furthermore, in order to make the model's outcomes more explainable, the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations (SHAP) framework. In the experimental study, other standalone quantum classifiers, namely, Quantum Support Vector Classifier (QSVC), Quantum Neural Network (QNN), and Variational Quantum Classifier (VQC) are applied and compared with classical machine learning classifiers such as Support Vector Machine (SVM), and Artificial Neural Network (ANN). The experimental results on the Cleveland dataset reveal the superiority of QSVC compared to the others, which explains its use in the proposed bagging model. The Bagging-QSVC model outperforms all aforementioned classifiers with an accuracy of 90.16% while showing great competitiveness compared to some state-of-the-art models using the same dataset. The results of the study indicate that quantum machine learning classifiers perform better than classical machine learning classifiers in predicting heart disease. In addition, the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classifiers.

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Section: Related Workmentioning

confidence: 99%

Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach

Abdulsalam¹,

Meshoul²,

Shaiba³

2023

Intelligent Automation &Amp; Soft Computing

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“…It serves as a mean to reduce the variance of a single KNN estimator by introducing randomization into its construction procedure [31] and then makes an ensemble out of it. The mechanism of KNN algorithm itself is to find the k nn samples closest to the query sample to be predicted based on a distance metric [32]. Then it performs the prediction based on averaging the information of these k nn nearest neighbors.…”

Section: Model Selectionmentioning

confidence: 99%

“…It is necessary to evaluate the path loss value in a very short time, so that the spatial distribution of electromagnetic fields can be quickly updated in response to the propagation environment changes. The complexity associated with the ML-based algorithms is tabulated in Table 4 as presented in [32]. The ensemble methods generally multiply the complexity of the original model by the number of ensembles within the model and replace the training size by the size of each ensemble.…”

Section: Computational Complexitymentioning

confidence: 99%

“…It is obvious from the table that the KNN algorithm has a lower complexity than the random forest algorithm. Moreover, KNN algorithm is defined as a memory-based non-parametric approach [32], hence it can immediately adapt to the new changes in the smart radio environment. Bagging ensemble based KNN algorithm frequently requires distance computation of k nn nearest neighbors for ensembles [28].…”

Section: Computational Complexitymentioning

confidence: 99%

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Large Intelligent Surface-Assisted Wireless Communication and Path Loss Prediction Model Based on Electromagnetics and Machine Learning Algorithms

Elshennawy¹

2022

PIER C

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This paper presents the application of machine learning-based approach toward prediction of path loss for the large intelligent surface-assisted wireless communication in smart radio environment. Two bagging ensemble methods, namely K-nearest neighbor and random forest, are exploited to build the path loss prediction models by using the training dataset. To generate the data samples without having to run measurement campaign, a path loss model is developed owning to the similarity between the large intelligent surface-assisted wireless communication and the reflector antenna system. Simple path loss expression is deduced from the system gain of the reflector antenna system, and it is used to generate the data samples. Simulation results are presented to verify the prediction accuracy of the path loss predictions models. The prediction performances of the trained path loss models are assessed based on the complexity and accuracy metrics, including R 2 score, mean absolute error, and root mean square error. It is demonstrated that the machine learning-based models can provide high prediction accuracy and acceptable complexity. The K-nearest neighbor algorithm outperforms random forest algorithm, and it has smaller prediction errors.

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“…A combination of k-nearest neighbor (KNN) and logistic regression is used for the ensemble learning framework that predict cardiovascular diseases. Data imbalance is managed via synthetic minority over-sampling technique (SMOTE) method [23]. Independent component analysis (ICA) is used for the dimensionality reduction to implement the lung cancer detection algorithm using the AdaBoost based ensemble learning method [24].…”

Section: Introductionmentioning

confidence: 99%

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

Shaikh

Parvati

Biradar

2022

IJEECS

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This paper <span lang="EN-US">implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.</span>

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An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases

Cited by 96 publications

References 41 publications

Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach

Explainable Heart Disease Prediction Using Ensemble-Quantum Machine Learning Approach

Large Intelligent Surface-Assisted Wireless Communication and Path Loss Prediction Model Based on Electromagnetics and Machine Learning Algorithms

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

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