Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Admassu, Tsehay

doi:10.46604/peti.2023.10101

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Calculating the contribution of each feature in the model is a challenging task. We employ the Local Interpretable Model-Agnostic Explanation (LIME) and Shapley Additive Explanation Algorithm (SHAP) [ 48 ] in our research to provide explanations for LPI-MFF. These methods investigate the contribution of the extracted features by visualizing the high contributory features from the whole feature set using machine learning algorithms [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

Predicting lncRNA–protein interactions through deep learning framework employing multiple features and random forest algorithm

Liang,

Yin,

Zhang

et al. 2024

BMC Bioinformatics

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RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein–protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

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Section: Resultsmentioning

confidence: 99%

Predicting lncRNA–protein interactions through deep learning framework employing multiple features and random forest algorithm

Liang,

Yin,

Zhang

et al. 2024

BMC Bioinformatics

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“…Considering simpler and more interpretable models like Decision Trees or Logistic Regression, especially when clinical decision-making requires transparency. Employ local interpretable models like Local Interpretable Model-Agnostic Explanations (LIME) or SHapley Additive exPlanations (SHAP), which is a game theoretic approach, to explain individual predictions [192][193][194][195][196][197][198]. These models provide explanations and visualizations for specific instances, which can be valuable in healthcare decision-making since they also help healthcare professionals to understand model outputs [199,200].…”

Section: Model Interpretability and Explainabilitymentioning

confidence: 99%

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Naser,

Majeed,

Alsabah

et al. 2024

Algorithms

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Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning’s role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area.

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“…The proposed block scaling approach is inspired by the concept of "local interpretable model-agnostic explanations" (LIME) [22]. The LIME approach has been successfully applied to applications such as chronic heart disease detection [23].…”

Section: Bsq Approachmentioning

confidence: 99%

Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling

Shingchern D. You,

Kai-Rong Lin,

Chien-Hung Liu

2023

Int. j. eng. technol. innov.

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This paper proposes an approach called block scaling quality (BSQ) for estimating the prediction accuracy of a deep network model. The basic operation perturbs the input spectrogram by multiplying all values within a block by , where is equal to 0 in the experiments. The ratio of perturbed spectrograms that have different prediction labels than the original spectrogram to the total number of perturbed spectrograms indicates how much of the spectrogram is crucial for the prediction. Thus, this ratio is inversely correlated with the accuracy of the dataset. The BSQ approach demonstrates satisfactory estimation accuracy in experiments when compared with various other approaches. When using only the Jamendo and FMA datasets, the estimation accuracy experiences an average error of 4.9% and 1.8%, respectively. Moreover, the BSQ approach holds advantages over some of the comparison counterparts. Overall, it presents a promising approach for estimating the accuracy of a deep network model.

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Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Cited by 6 publications

References 17 publications

Predicting lncRNA–protein interactions through deep learning framework employing multiple features and random forest algorithm

Predicting lncRNA–protein interactions through deep learning framework employing multiple features and random forest algorithm

A Review of Machine Learning’s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges

Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling

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