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... 6a-6c and 7) showing relationships between the reduced features of the dataset and the column to be predicted "Diagnosis", domain knowledge-based pattern analysis was made and those patterns were designed as rules to create some sort of multivariate constrained rules. After these rules were included, the efficiency of all the classifiers enhanced (Fig. 8). 95 percent precision was given by SVM with Gaussian kernel as shown in the last row of the Table 2 which is more than the standard ensemble algorithm's accuracy on this ...
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Heart disease has been the leading cause of a huge number of deaths in recent years. As a result, an accurate and feasible system is required to diagnose this disease early to provide better treatment. Advances in machine learning have the potential to enhance healthcare access. Given the importance of a crucial organ like the heart, medical profes...
Citations
... Meda and Bhogapathi (2022) developed the fuzzy neural-genetic algorithm-based model to classify and categorize cardiac diseases using UCI Cleveland Heart Disease (UCI) Dataset. Santhi and Renuka (2020) achieved 96% classification accuracy for five classes of cardiac using various ML algorithms using the UCI dataset. Channabasavaraju and Vinayakamurthy (2020) used Random Forest Feature Selection (RFS) strategy to extract features from the UCI dataset to improve the prediction accuracy of heart disease. ...
Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images. © 2022 Sibu Cyriac, Sivakumar R. and Nidhin Raju. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
