TY - GEN
T1 - A Robust Deep Learning based Prediction System of Heart Disease using a Combination of Five Datasets
AU - Biswas, Ritu
AU - Beeravolu, Abhijith Reddy
AU - Karim, Asif
AU - Azam, Sami
AU - Hasan, Md Tanvir
AU - Alam, Md Soriful
AU - Ghosh, Pronab
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - All across the world, heart disease is regarded as a fatal disease. Heart disease is a condition that affects both men and women equally and may be a major cause of death around the world. Early diagnosis of this condition is critical for everyone in order to reduce mortality rates day by day. Chronic kidney disease dataset, from UCI machine learning library, having 1190 samples with 14 characteristics has been used for this study. To make this research more potent, both Machine learning (ML) and Deep learning (DL) techniques were used to detect the sickness early. The data was normalized by standard scaler for having a class varience issue. We then used three deep learning techniques namely Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Long Short Term Memory (LSTM) with two other general machine learning approaches such as Decision Tree and Support Vector Machine (SVM). To show a replication study, the overall experiments were done based on the three different random subsets. For the classification measurement, we also employ the ROC and the AUC curves. Several promising outcomes have been achieved. We calculated accuracy, precision, sensitivity, specificity, and F1-score. CNN provided the best results, with an accuracy of 99.16%.
AB - All across the world, heart disease is regarded as a fatal disease. Heart disease is a condition that affects both men and women equally and may be a major cause of death around the world. Early diagnosis of this condition is critical for everyone in order to reduce mortality rates day by day. Chronic kidney disease dataset, from UCI machine learning library, having 1190 samples with 14 characteristics has been used for this study. To make this research more potent, both Machine learning (ML) and Deep learning (DL) techniques were used to detect the sickness early. The data was normalized by standard scaler for having a class varience issue. We then used three deep learning techniques namely Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Long Short Term Memory (LSTM) with two other general machine learning approaches such as Decision Tree and Support Vector Machine (SVM). To show a replication study, the overall experiments were done based on the three different random subsets. For the classification measurement, we also employ the ROC and the AUC curves. Several promising outcomes have been achieved. We calculated accuracy, precision, sensitivity, specificity, and F1-score. CNN provided the best results, with an accuracy of 99.16%.
KW - confusion matrix
KW - deep Learning
KW - Heart disease
KW - machine learning
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85131912921&partnerID=8YFLogxK
U2 - 10.1109/ICCTA54562.2021.9916601
DO - 10.1109/ICCTA54562.2021.9916601
M3 - Conference Paper published in Proceedings
AN - SCOPUS:85131912921
T3 - 31st International Conference on Computer Theory and Applications, ICCTA 2021 - Proceedings
SP - 223
EP - 228
BT - 31st International Conference on Computer Theory and Applications, ICCTA 2021 - Proceedings
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 31st International Conference on Computer Theory and Applications, ICCTA 2021
Y2 - 11 December 2021 through 13 December 2021
ER -