Predicting Heart Attack Through Explainable Artificial Intelligence

Aghamohammadi, Mehrdad; Madan, Manvi; Hong, Jiong; Watson, Ian

doi:10.1007/978-3-030-22741-8_45

Cited by 24 publications

(4 citation statements)

References 8 publications

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“…Several kinds of DenseNet systems have been developed which can be tried and the current DenseNet can be replaced by [ 146 , 147 , 148 ]; as part of the extension to this study, more AI models can be explored and can incorporate the use of the HDL model for binary or multiclass-based classification [ 128 ] framework. Explainable AI is an emerging field and many new strategies can be incorporated [ 47 , 50 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 ]. New techniques have evolved such as SHAP [ 52 , 158 ] and UMAP [ 159 ].…”

Section: Discussionmentioning

confidence: 99%

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Suri¹,

Agarwal²,

Chabert

et al. 2022

Diagnostics

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Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

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Section: Discussionmentioning

confidence: 99%

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Suri¹,

Agarwal²,

Chabert

et al. 2022

Diagnostics

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“…Aghamohammadi et al [13] had proposed a hybrid system comprising GA, ANFIS, and K-fold cross-validation for prediction of heart disease experimented over Cleveland dataset from UCI taking 14 features. Model efficiency was evaluated using ACC, SENS, and SPEC with result as 84.43%, 91.15%, and 79.16%, respectively.…”

Section: Related Workmentioning

confidence: 99%

Neural Fuzzy Hybrid Rule-Based Inference System with Test Cases for Prediction of Heart Attack Probability

Jha

Henge

Mandal

et al. 2022

Mathematical Problems in Engineering

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Heart disease has reached to the number one position in last decade in terms of mortality rate, and more wretchedly, heart attack has affected life in 80% of the cases. Cardiac arrest is an incurable incongruity that requires special treatment and cure. It has been a key research area for many years, and the number of researchers across the globe is devoted toward finding the optimal solution to avoid the ill-effect of this disease. Along with predicting heart disease, if focus moves towards prevention of heart attack as well, then this could result in major life saver area for masses. This research work is fully devoted toward finding out the probability of heart attack so that people can take preventive measure before it hit the wall. This research proposed the neural fuzzy inference system (NFIS) to represent the training data formed from the n-dimensions of functions. The NFIS consists of error computing module to improve the learning instructions when the errors have been measured, initially the membership functions are defined, and the parameters of membership functions are activated and learnt through when needed for an operation. The proposed methodology has experimented with sample test cases on Cleveland heart disease dataset from University of California Irvine (UCI) repository with the integration of supporting dependable and nondependable parameters, causing-factors, and data-matrices. This research has integration more than 13000 fuzzification rules to generate best decision-making, normalization process, planting techniques to create the feasibility to compute the heart attack probability and achieved 94 percentage of accuracy. This research can be extendable to build auto-altering and advise system with integration hardware peripheral circuit devices.

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“…This adheres to ethical concern and regulatory considerations that need to be made within the domain, should there be bias or discriminatory results. Healthcare is susceptible to such doubts; and XAI methods have been thus developed and applied in e.g., [6], [7] and [8]. It is believed that XAI will provide the much needed trust in human-AI collaboration for critical applications in healthcare.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records

Duell

Fan

Burnett

et al. 2021

2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

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eXplainable Artificial Intelligence (XAI) aims to provide intelligible explanations to users. XAI algorithms such as SHAP, LIME and Scoped Rules compute feature importance for machine learning predictions. Although XAI has attracted much research attention, applying XAI techniques in healthcare to inform clinical decision making is challenging. In this paper, we provide a comparison of explanations given by XAI methods as a tertiary extension in analysing complex Electronic Health Records (EHRs). With a large-scale EHR dataset, we compare features of EHRs in terms of their prediction importance estimated by XAI models. Our experimental results show that the studied XAI methods circumstantially generate different top features; their aberrations in shared feature importance merit further exploration from domain-experts to evaluate human trust towards XAI.

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Predicting Heart Attack Through Explainable Artificial Intelligence

Cited by 24 publications

References 8 publications

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Neural Fuzzy Hybrid Rule-Based Inference System with Test Cases for Prediction of Heart Attack Probability

A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records

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