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Explainable heart disease prediction Using Ensemble-Quantum Machine Learning Approach

Intelligent Automation & Soft Computing 2023年第4期36卷 761-779页

作者： Ghada Abdulsalam Souham Meshoul Hadil Shaiba College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Nowadays,quantum machine learning is attracting great interest in a wide range ofﬁelds due to its potential superior performance and *** massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcareﬁ*** disease seriously threa-tens human health since it is the leading cause of death *** machine learning methods can propose effective solutions to predict heart disease and aid in early *** this study,an ensemble machine learning model based on quantum machine learning classiﬁers is proposed to predict the risk of heart *** proposed model is a bagging ensemble learning model where a quantum support vector classiﬁer was used as a base classiﬁ***-more,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)*** the experimental study,other stand-alone 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 Sup-port Vector Machine(SVM),and Artiﬁcial Neural Network(ANN).The experi-mental results on the Cleveland dataset reveal the superiority of QSVC compared to the others,which explains its use in the proposed bagging *** 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 *** results of the study indicate that quantum machine learning classiﬁers perform better than classical machine learning classi-ﬁers in predicting heart *** addition,the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classiﬁers.

关键词： Machine learning ensemble learning quantum machine learning explainable machine learning heart disease prediction

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An Improved Ensemble Learning Approach for heart disease prediction Using Boosting Algorithms

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Computer Systems Science & Engineering 2023年第9期46卷 3993-4006页

作者： ShahidMohammad Ganie Pijush Kanti Dutta Pramanik Majid BashirMalik Anand Nayyar Kyung Sup Kwak School of Business Woxsen UniversityHyderabadTelangana502345India School of Computing Science&Engineering Galgotias UniversityGreater NoidaUP 203201India Department of Computer Sciences Baba Ghulam Shah Badshah UniversityRajouri185234India Graduate School Faculty of Information TechnologyDuy Tan UniversityDa Nang50000Vietnam Department of Information and Communication Engineering Inha University22212Korea

Cardiovascular disease is among the top five fatal diseases that affect lives ***,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier *** learning(ML)techniques are used to assist healthcare providers in better diagnosing heart *** study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart *** dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML *** data analysis is performed to find the characteristics of data samples about descriptive and inferential ***,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation *** were recorded before and after the data preprocessing techniques were *** of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed *** proposed model yielded better results with gradient boosting in terms of precision,recall,and *** attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

关键词： heart disease prediction machine learning classifiers ensemble approach XGBoost AdaBoost gradient boost

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heart disease Classification Using Multiple K-PCA and Hybrid Deep Learning Approach

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Computer Systems Science & Engineering 2022年第6期41卷 1273-1289页

作者： S.Kusuma Dr.Jothi K.R School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

One of the severe health problems and the most common types of heartdisease (HD) is Coronary heart disease (CHD). Due to the lack of a healthy lifestyle, HD would cause frequent mortality worldwide. If the heart attack occurswithout any symptoms, it cannot be cured by an intelligent detection *** effective diagnosis and detection of CHD should prevent human ***, intelligent systems employ clinical-based decision support approachesto assist physicians in providing another option for diagnosing and detecting *** paper aims to introduce a heart disease prediction model including phaseslike (i) Feature extraction, (ii) Feature selection, and (iii) Classification. At first,the feature extraction process is carried out, where the features like a time-domainindex, frequency-domain index, geometrical domain features, nonlinear features,WT features, signal energy, skewness, entropy, kurtosis features are extractedfrom the input ECG signal. The curse of dimensionality becomes a severe *** paper provides the solution for this issue by introducing a new ModifiedPrincipal Component Analysis known as Multiple Kernel-based PCA for dimensionality reduction. Furthermore, the dimensionally reduced feature set is thensubjected to a classification process, where the hybrid classifier combining bothRecurrent Neural Network (RNN) and Restricted Boltzmann Machine (RBM)is used. At last, the performance analysis of the adopted scheme is compared overother existing schemes in terms of specific measures.

关键词： heart disease prediction ecg recurrent neural network pca restricted boltzmann machine

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Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

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Intelligent Automation & Soft Computing 2023年第6期36卷 3675-3690页

作者： V.Gowri V.Vijaya Chamundeeswari Department of CSE SRM Institute of Science and TechnologyRamapuram CampusChennaiTamilnaduIndia Department of CSE Saveetha Engineering CollegeChennaiTamilnaduIndia

Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical ***,the tra-ditional decision forest(DF)algorithms have lower classification accuracy and cannot handle high-dimensional feature space *** this work,we pro-pose a bootstrap decision forest using penalizing attributes(BFPA)algorithm to predict heart disease with higher *** work integrates a significance-based attribute selection(SAS)algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac *** pro-posed SAS algorithm is used to determine the correlation among attributes and to select the optimum subset of feature space for learning and testing *** selects the optimal number of learning and testing data points as well as the density of trees in the forest to realize higher prediction accuracy in classifying imbalanced datasets *** effectiveness of the developed classifier is cautiously verified on the real-world database(i.e.,heart disease dataset from UCI repository)by relating its enactment with many advanced approaches with respect to the accuracy,sensitivity,specificity,precision,and intersection over-union(IoU).The empirical results demonstrate that the intended classification approach outdoes other approaches with superior enactment regarding the accu-racy,precision,sensitivity,specificity,and IoU of 94.7%,99.2%,90.1%,91.1%,and 90.4%,***,we carry out Wilcoxon’s rank-sum test to determine whether our proposed classifier with feature selection method enables a noteworthy enhancement related to other classifiers or *** the experimental results,we can conclude that the integration of SAS and BFPA outperforms other classifiers recently reported in the literature.

关键词： Data classification decision forest feature selection healthcare data heart disease prediction penalizing attributes

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A Novel Approach to heart Failure prediction and Classification through Advanced Deep Learning Model

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World Journal of Cardiovascular diseases 2023年第9期13卷 586-604页

作者： Abdalla Mahgoub Dubai United Arab Emirates

In this study, the author will investigate and utilize advanced machine learning models related to two different methodologies to determine the best and most effective way to predict individuals with heart failure and cardiovascular diseases. The first methodology involves a list of classification machine learning algorithms, and the second methodology involves the use of a deep learning algorithm known as MLP or Multilayer Perceptrons. Globally, hospitals are dealing with cases related to cardiovascular diseases and heart failure as they are major causes of death, not only for overweight individuals but also for those who do not adopt a healthy diet and lifestyle. Often, heart failures and cardiovascular diseases can be caused by many factors, including cardiomyopathy, high blood pressure, coronary heart disease, and heart inflammation [1]. Other factors, such as irregular shocks or stress, can also contribute to heart failure or a heart attack. While these events cannot be predicted, continuous data from patients’ health can help doctors predict heart failure. Therefore, this data-driven research utilizes advanced machine learning and deep learning techniques to better analyze and manipulate the data, providing doctors with informative decision-making tools regarding a person’s likelihood of experiencing heart failure. In this paper, the author employed advanced data preprocessing and cleaning techniques. Additionally, the dataset underwent testing using two different methodologies to determine the most effective machine-learning technique for producing optimal predictions. The first methodology involved employing a list of supervised classification machine learning algorithms, including Naïve Bayes (NB), KNN, logistic regression, and the SVM algorithm. The second methodology utilized a deep learning (DL) algorithm known as Multilayer Perceptrons (MLPs). This algorithm provided the author with the flexibility to experiment with different layer sizes and activation

关键词： heart disease prediction Cardiovascular disease Machine Learning Algorithms Lazy Predict Multilayer Perceptrons (MLPs) Data Science Techniques and Analysis Deep Learning Activation Functions

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