Computational modelling to diagnose cardiovascular disease and its complications
Creators: name: Hamed, Sohayla
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 461199
subject: Machine learning not elsewhere classified
classificationCode: 320101
subject: Cardiology (incl. cardiovascular diseases)
classificationCode: 461299
subject: Software engineering not elsewhere classified

description: Myocardial infarction (MI), also known as heart attack, is a life threatening heart disease caused by blood retention as a result of the blockage of one or more of the coronary arteries. MI causes damages to parts of the heart muscle, or even their complete death. MI is a major health concern worldwide, with millions of cases reported annually. The latest statistics indicate that the death rate from MI is 30% within 30 days if left untreated. This thesis presents the development of a multimodal web application aimed at diagnosing MI using a combination of risk factor tabular data, electrocardiogram signals (ECGs), and echocardiogram videos. <br>The proposed web application is compromised of three frameworks, one for analyzing risk factor data, another for ECGs and the third for echocardiogram videos. <br>For the risk factor framework, ML methods, including Random Forest (RF) classifiers, Decision Tree (DT) classifiers, Support Vector Machines (SVM), Logistic Regression (LR), and Gradient Boosting (GB), have been assessed for their efficacy in predicting heart attacks through the analysis of extensive patient data. The experimental findings show that the GB model outperforms the other models in terms of accuracy, with 98% training accuracy and 96% testing accuracy, and is less pruned to overfitting (its F1 scorch is 97% for training and 96% for testing). These findings offer important new information for cardiovascular health monitoring and diagnosis in clinical settings. <br>For the ECG framework, two raw signal datasets were utilized for training and testing purposes - the Physikalisch-Technische Bundesanstalt (PTB) Diagnostic ECG dataset and the MIT Arrhythmia Dataset. The two datasets were categorized by selecting the most appropriate morphological ECG features, such as lead vz. In addition, stochastic batch-making was used prior to running any model. The categorized data was classified by running different deep neural network models (DNN), followed by some optimization to yield better metrics. In terms of 3-channel-based non-optimized architecture, ResNet50 reported the best results with an average accuracy of 98.67% while ConvNetQuake reported the second-best results with an average 88% accuracy. To optimize those two best performing models, a maximum of 100 particles were used and the inertia was decreased automatically to lie between 0.4 and 0.9. <br>For echocardiography, a framework was designed that starts by extracting the frames of a echocardiogram then segmenting the left ventricle wall out of each frame using a deep learning model. The segmenation model proposed was a LadderNet that was trained and tested on the benchmark HMC-QU dataset’s echocardio?gram videos and their corresponding ground-truth segmentation masks. The proposed segmentation model demonstrated excellent performance with an IoU of 97.32%, an F1 score of 97.40%, a testing accuracy of 99.78%, a testing pre?cision of 97.50%, and a testing sensitivity of 97.31%. The next phase of the framework divides each segmented left ventricle wall out of each frame into six sections whose displacements are tracked throughout the consecutive frames. The final phase of the echocardiogrpahy framework utilizes a machine learning model to classify if the input echocardiogram displays MI based on the aforementioned displacements from the second phase. Five traditional ML classification models, RF, DT, SVM, LR, and K-Nearest Neighbours (KNN) were assessed for their efficacy in identifying MI from the displacements of the left ventricle wall’s sections. LR outperformed the rest of the models and provided consistent results both when trained and tested on the LadderNet’s predicted masks and on HMC-QU’s ground-truth masks. LR achieved a testing accuracy of 85.71%, a testing precision of 92.30%, and an F1 score of 88.89%. <br>As supplementary work in addition to the web application, multi-label machine learning techniques were used to predict and categorise several MI consequences, such heart failure, arrhythmias, and cardiogenic shock. Advanced machine learning models, such as binary relevance, classifier chains, label powerset, and RAKEL-D, were used with large patient datasets and characteristics, such as biomarker levels, clinical history, and demographic data. The results showed that the Label Powerset model performs better than other classifiers in correctly recognising several concurrent complications. It had an accuracy of 0.42, a hamming loss of 0.062, a Jaccard index of 0.63, and an F1 score ranging from 0.62 to 0.645 over 5 folds. This development emphasises how machine learning (ML) might improve patient care and outcomes in MI situations by streamlining diagnostic procedures and enabling focused therapies.<br>