Machine learning-based heart disease diagnosis: A systematic literature review

Ahsan, Md Manjurul; Siddique, Zahed

doi:10.1016/j.artmed.2022.102289

Cited by 172 publications

(79 citation statements)

References 46 publications

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“…The effectiveness of MNIM in this research motivates us to extend its applicability to more complex infectious disease models. Further work should reconsider applying these techniques in developing a dynamic model for other conditions such as heart disease [35][36][37], suicide prevention [38], and combine the suggested model with machine learning techniques in developing optimal solutions for infectious diseases such as COVID-19, Pneumonia, and so on [39][40][41].…”

Section: Discussionmentioning

confidence: 99%

Accurate Numerical Treatment on a Stochastic SIR Epidemic Model with Optimal Control Strategy

et al. 2022

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In this paper, a numerical study has been undertaken on the susceptible-infected-recovered (SIR) epidemic model that encompasses the mechanisms of the evolution of disease transmission; a prophylactic vaccination strategy in the susceptible populations, depending on the infective individuals. We furnish numerical and graphical simulation combined with explicit series solutions of the proposed model using the New Iterative Method (NIM) and Modified New Iterative Method (MNIM). The analytic-numeric New Iterative Method failed to deliver accurate solution for the large time domain. A new reliable algorithm based on NIM, the coupling of the Laplace transforms, and the New Iterative method is called Modified New Iterative Method (MNIM) which is presented to enhance the validity domain of NIM techniques. The convergence analysis of the MNIM has also been illustrated. The simulation results show that the vaccination strategy can slow down the spread of the epidemic rapidly. Numerical results illustrate the excellent performance of the MNIM and show that the modified method is much more accurate than the NIM.

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Section: Discussionmentioning

confidence: 99%

Accurate Numerical Treatment on a Stochastic SIR Epidemic Model with Optimal Control Strategy

et al. 2022

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“…This process continues until each portion has been utilized for validation. It is a standard technique used for assessment [33].…”

Section: Dataset Splitting Using K-fold Cross-validationmentioning

confidence: 99%

Heart Disease Risk Prediction Expending of Classification Algorithms

Mary¹,

Khan²,

Asiri³

et al. 2022

Computers, Materials &Amp; Continua

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Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today's lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease the error rate and enhance the accuracy of HDP assessment measures. However, in order to discover the optimal HDP solution, this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California, Irvine (UCI) machine learning repository. In a comparative analysis, Mean Absolute Error (MAE), Relative Absolute Error (RAE), precision, recall, fmeasure, and accuracy are used to evaluate Linear Regression (LR), Decision Tree (J48), Naive Bayes (NB), Artificial Neural Network (ANN), Simple Cart (SC), Bagging, Decision Stump (DS), AdaBoost, Rep Tree (REPT), and Support Vector Machine (SVM). Overall, the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate, with RAE of 33.2631 and MAE of 0.165, the precision of 0.841, recall of 0.835, f-measure of 0.833, and accuracy of 83.49 percent for the dataset gathered from UCI. The SC improves accuracy and reduces the error rate for the Kaggle dataset, which is 3.30% for RAE, 0.016 percent for MAE, 0.984% for precision, 0.984 percent for recall, 0.984 percent for f-measure, and 98.44% for accuracy.

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“…To ease the early detection of ASD, this method can be a milestone for the primary screening of the ASD or normal child. Recent studies demonstrate the potentiality of the deep neural network, particularly the application of CNN models in various disease diagnosis [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Due to its remarkable ability to learn by automatically extracting the hidden features from a large volume of images, convolutional neural networks (CNNs) are the widely used feature extractors for object detection or image classification work.…”

Section: Introductionmentioning

confidence: 99%

Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach

et al. 2022

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Autism spectrum disorder (ASD) is a neurological illness characterized by deficits in cognition, physical activities, and social skills. There is no specific medication to treat this illness; only early intervention can improve brain functionality. Since there is no medical test to identify ASD, a diagnosis might be challenging. In order to determine a diagnosis, doctors consider the child’s behavior and developmental history. The human face can be used as a biomarker as it is one of the potential reflections of the brain and thus can be used as a simple and handy tool for early diagnosis. This study uses several deep convolutional neural network (CNN)-based transfer learning approaches to detect autistic children using the facial image. An empirical study is conducted to select the best optimizer and set of hyperparameters to achieve better prediction accuracy using the CNN model. After training and validating with the optimized setting, the modified Xception model demonstrates the best performance by achieving an accuracy of 95% on the test set, whereas the VGG19, ResNet50V2, MobileNetV2, and EfficientNetB0 achieved 86.5%, 94%, 92%, and 85.8%, accuracy, respectively. Our preliminary computational results demonstrate that our transfer learning approaches outperformed existing methods. Our modified model can be employed to assist doctors and practitioners in validating their initial screening to detect children with ASD disease.

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Machine learning-based heart disease diagnosis: A systematic literature review

Cited by 172 publications

References 46 publications

Accurate Numerical Treatment on a Stochastic SIR Epidemic Model with Optimal Control Strategy

Accurate Numerical Treatment on a Stochastic SIR Epidemic Model with Optimal Control Strategy

Heart Disease Risk Prediction Expending of Classification Algorithms

Empirical Study of Autism Spectrum Disorder Diagnosis Using Facial Images by Improved Transfer Learning Approach

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