Machine Learning Algorithms for The Classification of Cardiovascular Disease- A Comparative Study

Jinjri, Wada Mohammed; Keikhosrokiani, Pantea; Abdullah, Nasuha Lee

doi:10.1109/icit52682.2021.9491677

Cited by 25 publications

(9 citation statements)

References 19 publications

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“…Table 14 shows the comparison of the existing research and the proposed classifier. Khan et al 21 used LSTM as a classifier and concluded the accuracy of 91.39%, Al-Naami et al 59 used ANFIS which managed to achieve 89% accuracy, Nogueira et al 55 used SVM, in which the accuracy is 82.33%, Li et al 57 used CNN with 86.90% accuracy. Sharma et al 56 have also done research using the same dataset that is used in this research.…”

Section: Analysis and Resultsmentioning

confidence: 99%

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Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

et al. 2023

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Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.

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Section: Analysis and Resultsmentioning

confidence: 99%

“…Moving forward, in Ref. 55 , the authors have also used a challenge database named Physionet/Computing in Cardiology Challenge. The database provides the training set consists of both EGG and PCG signals.…”

Section: Analysis and Resultsmentioning

confidence: 99%

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

et al. 2023

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“…The study [21] compared the five most powerful ML platforms for classifying CVD data using the heart disease Cleveland dataset, which contains 303 instances and 76 attributes from the UCI ML repository. The classifiers considered were SVM, KNN, LR, DT, and NB.…”

Section: Related Workmentioning

confidence: 99%

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

Omar Sadeq Salman,

Abdul Latiff,

Syed Arifin

et al. 2024

Elektrika

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Current environmental conditions and human lifestyles have resulted in the emergence of numerous diseases. The medical field generates an enormous amount of new data each year for remote monitoring of patients. Due to increased data growth in the medical and healthcare industries, accurate medical data analysis has been advantageous to early patient care. However, physicians often face challenges in accurately diagnosing diseases in patients far from hospitals. Therefore, utilizing remote patient systems (telemedicine systems) due to the complexities associated with their chronic conditions. On the other hand, predicting illness is also a challenging task. Thus, data extracted from heterogeneous, fast-flowing, and reliable sources is crucial for decision-making and disease prediction. This research paper aims to utilize supervised Machine Learning (ML) techniques to predict chronic diseases such as heart and hypertension based on the patient’s features or symptoms by analyzing patient data collected by sensors and sources enabled by the Internet of Medical Things (IoMT). Supervised ML technology in Hadoop and Spark environments is employed to guarantee that this classification accurately identifies individuals with chronic illnesses. The methods are evaluated using 55,680 patient records to discover the proper match between the data set and the final disease-predicted result. The results demonstrate that the proposed procedure employing the Decision Tree (DT) algorithm is 94% accurate, and DT outperforms the other four ML algorithms. This includes the Support Vector Machine (SVM), a Naive Bayes (NB) model, Random Forest (RF), and Logistic Regression (LR) in terms of both performance and accuracy metrics (precision, recall, and F-score).

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“…Using a 10-fold cross-validation method, the experimenters determined the estimation of their detection models’ objectivity. According to their findings, LR obtained 91 percent accuracy, and BFNN achieved 88 percent accuracy [ 43 ]. In [ 44 ], they examined the performance of computational intelligence systems for identifying cardiac disease.…”

Section: Related Workmentioning

confidence: 99%

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Haq,

Bazai,

Fatima

et al. 2023

Diagnostics

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Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals’ lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively.

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Machine Learning Algorithms for The Classification of Cardiovascular Disease- A Comparative Study

Cited by 25 publications

References 19 publications

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

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