Downloads: 5 | Views: 371 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1
Comparative Studies | Information Technology | United Kingdom | Volume 12 Issue 8, August 2023 | Popularity: 5.1 / 10
Comparative Analysis for Predicting Cardiovascular Diseases Using Machine Learning and Deep Learning Approaches
Chidozie Louis Uzoegwu, Farah Ahmed, Honglei Li
Abstract: This research investigates the potential of using physiological signs, including respiratory rate, blood pressure, body temperature, heart rate, and oxygen saturation, to predict cardiovascular disease (CVD) in humans. Machine learning (ML) and deep learning (DL) models were employed to determine the most effective prediction model by comparing their performance metrics to a previous study conducted by Ashfaq et al. in 2022. Ashfaq's research utilized three parameters (body temperature, heart rate, and oxygen saturation) and achieved a top performance of 96% using K-Nearest Neighbour (KNN). The analysis utilized a dataset obtained from the MIMIC-III clinical database. Four models were evaluated: Random Forest (RF), K-Nearest Neighbour (KNN) as part of the ML approach, and Multi-Layer Perception (MLP) and Convolutional Neural Network (CNN) as part of the DL approach. Performance evaluation was conducted using five measurement metrics, namely accuracy, precision, recall, F1-score, and ROC AUC. The findings demonstrate significant performance by all models, with MLP exhibiting the highest overall performance measures, including an accuracy of 99%, precision of 99%, recall of 99%, F1-score of 98%, and ROC AUC of 98%. The RF model closely followed MLP in terms of performance. This research provides valuable insights for medical researchers, individuals, academies, analysts, and artificial intelligence enthusiasts, informing them about research ideas and areas for improvement, particularly in the health sector, specifically in the management of CVD in humans. Furthermore, the integration of these models into monitoring systems using body sensors could facilitate prompt emergency intervention for CVD patients. In comparison to the previous study by Ashfaq et al., this research expands the parameter set to include five body parameters, enhancing the accuracy and effectiveness of CVD prediction. The utilization of advanced ML and DL models highlights the potential for significant improvements in the field of cardiovascular disease prediction and management.
Keywords: cardiovascular diseases, machine learning, deep learning, ML, DL, comparative analysis, CVD
Edition: Volume 12 Issue 8, August 2023
Pages: 945 - 964
DOI: https://www.doi.org/10.21275/SR23809044938
Please Disable the Pop-Up Blocker of Web Browser
Verification Code will appear in 2 Seconds ... Wait