This study seeks to enhance an Artificial Intelligence (AI) system for identifying medical issues using deep learning (DL) techniques. Conventional methods often struggle to predict health conditions and provide effective solutions. A re-modelled convolutional neural network (RCNN) is introduced, featuring optimized activation functions in its convolutional layers and incorporating dense, fully connected layers. The efficiency of the RCNN algorithm is validated by comparing it with other advanced deep learning algorithms. Using available datasets, the study evaluates the accuracy of the DL system in detecting medical conditions within the Python Jupyter environment. Performance metrics, including F1 score, recall, accuracy, and precision, are used to assess the effectiveness of the proposed RCNN model.
Keywords
Deep Learning, Re-Modelled Convolutional Neural Network (RCNN), Performance Metrics.
K. V. Rao and L. M. Gladence, “Support vector machine-based disease classification model employing hasten eagle Cuculidae search optimization,” Concurrency and Computation: Practice and Experience, vol. 34, no. 25, Aug. 2022, doi: 10.1002/cpe.7259.
A Research on Application of Human-Robot Interaction using Artifical Intelligence,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9S2, pp. 784–787, Aug. 2019, doi: 10.35940/ijitee. i1162.0789s219.
L. Mary Gladence, T. Ravi, M. Karthi, “Heart Disease Prediction using Naïve Bayes Classifier-Sequential Pattern Mining,” the International Journal of Applied Engineering Research, volume. 9, no. 21, pp. 8593-8602, 2014.
S. Premanand and S. Sugunavathy, “Enhancing the Performance of Multi-parameter Patient Monitors by Homogeneous Kernel Maps,” Application of Computational Intelligence to Biology, pp. 1–7, 2016, doi: 10.1007/978-981-10-0391-2_1.
H. Sun and J. Pan, “Heart Disease Prediction Using Machine Learning Algorithms with Self-Measurable Physical Condition Indicators,” Journal of Data Analysis and Information Processing, vol. 11, no. 01, pp. 1–10, 2023, doi: 10.4236/jdaip.2023.111001.
A. Ahdal, M. Rakhra, S. Badotra, and T. Fadhaeel, “An integrated Machine Learning Techniques for Accurate Heart Disease Prediction,” 2022 International Mobile and Embedded Technology Conference (MECON), pp. 594–598, Mar. 2022, doi: 10.1109/mecon53876.2022.9752342.
B. Dhande, K. Bamble, S. Chavan, and T. Maktum, “Diabetes & Heart Disease Prediction Using Machine Learning,” ITM Web of Conferences, vol. 44, p. 03057, 2022, doi: 10.1051/itmconf/20224403057.
P. Keikhosrokiani, A. B. Naidu A/P Anathan, S. Iryanti Fadilah, S. Manickam, and Z. Li, “Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony,” DIGITAL HEALTH, vol. 9, Jan. 2023, doi: 10.1177/20552076221150741.
S. Peyvandi et al., “Declining Incidence of Postoperative Neonatal Brain Injury in Congenital Heart Disease,” Journal of the American College of Cardiology, vol. 81, no. 3, pp. 253–266, Jan. 2023, doi: 10.1016/j.jacc.2022.10.029.
R. Teixeira, C. Rodrigues, C. Moreira, H. Barros, and R. Camacho, “Machine learning methods to predict attrition in a population-based cohort of very preterm infants,” Scientific Reports, vol. 12, no. 1, Jun. 2022, doi: 10.1038/s41598-022-13946-z.
A.-M. Suutari, J. Thor, A. Nordin, and K. A. Josefsson, “Improving heart failure care with an Experience-Based Co-Design approach: what matters to persons with heart failure and their family members?” BMC Health Services Research, vol. 23, no. 1, Mar. 2023, doi: 10.1186/s12913-023-09306-w.
C. White-Williams et al., “Patient experience in an interprofessional collaborative practice for underserved patients with heart failure,” Patient Experience Journal, vol. 10, no. 1, pp. 83–92, Apr. 2023, doi: 10.35680/2372-0247.1752.
N. V. L. M. K. Munagala, L. R. R. Langoju, A. D. Rani, and D. V. rama K. Reddy, “Optimised ensemble learning-based IoT-enabled heart disease monitoring system: an optimal fuzzy ranking concept,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 11, no. 5, pp. 1598–1614, Jan. 2023, doi: 10.1080/21681163.2022.2162439.
S. Ninni et al., “Hematopoietic Somatic Mosaicism Is Associated with an Increased Risk of Postoperative Atrial Fibrillation,” Journal of the American College of Cardiology, vol. 81, no. 13, pp. 1263–1278, Apr. 2023, doi: 10.1016/j.jacc.2023.01.036.
S. V. E. Sonia, R. Nedunchezhian, S. Ramakrishnan, and K. E. Kannammal, “An empirical evaluation of benchmark machine learning classifiers for risk prediction of cardiovascular disease in diabetic males,” International Journal of Healthcare Management, pp. 1–16, Jan. 2023, doi: 10.1080/20479700.2023.2170006.
R. K. Wagner, J. Moxley, C. Schatschneider, and F. A. Zirps, “A Bayesian Probabilistic Framework for Identification of Individuals with Dyslexia,” Scientific Studies of Reading, vol. 27, no. 1, pp. 67–81, Dec. 2022, doi: 10.1080/10888438.2022.2118057.
Dutta, T. Batabyal, M. Basu, and S. T. Acton, “An efficient convolutional neural network for coronary heart disease prediction,” Expert Systems with Applications, vol. 159, p. 113408, Nov. 2020, doi: 10.1016/j.eswa.2020.113408.
Rai and M. Mitra, “Lamb wave-based damage detection in metallic plates using multi-headed 1-dimensional convolutional neural network,” Smart Materials and Structures, vol. 30, no. 3, p. 035010, Feb. 2021, doi: 10.1088/1361-665x/abdd00.
M. G. Ragab et al., “A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction,” Sustainability, vol. 12, no. 23, p. 10090, Dec. 2020, doi: 10.3390/su122310090.
The authors confirm contribution to the paper as follows:
Conceptualization: Mohammad Azhar and Mary Gladence L;
Methodology: Mohammad Azhar;
Software: Mary Gladence L;
Writing- Original Draft Preparation: Mohammad Azhar and Mary Gladence L;
Validation: Mary Gladence L;
Writing- Reviewing and Editing: Mohammad Azhar and Mary Gladence L;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Mohammad Azhar
Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Mohammad Azhar and Mary Gladence L, “Enhancing Therapeutic Investigation Through AI Driven Convolutional Neural Network in Comparison with Deep Learning Techniques”, Journal of Machine and Computing, pp. 1055-1067, April 2025, doi: 10.53759/7669/jmc202505084.
