An Intelligent Heart Disease Prediction by Machine Learning Using Optimization Algorithm

Document Type : Research Paper

Authors

1 Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore, Tamil Nadu, India.

2 Computer Science and Engineering, Kings Engineering College, Chennai, Tamil Nadu, India.

3 Computer Science and Engineering, N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India.

4 Computer Science and Engineering, KIT-Kalaignar Karunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India.

5 Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India.

6 Computer Science and Engineering, M.K Umarasamy College of Engineering, Karur, Tamil Nadu, India.

Abstract

Heart and circulatory system diseases are often referred to as cardiovascular disease (CVD). The health and efficiency of the heart are crucial to human survival. CVD has become a primary cause of demise in recent years. According to data provided by the World-Health-Organization (WHO), CVD were conscientious for the deaths of 18.6M people in 2017. Biomedical care, healthcare, and disease prediction are just few of the fields making use of cutting-edge skills like machine learning (ML) and deep learning (DL). Utilizing the CVD dataset from the UCI Machine-Repository, this article aims to improve the accuracy of cardiac disease diagnosis. Improved precision and sensitivity in diagnosing heart disease by the use of an optimization algorithm is possible. Optimization is the process of evaluating a number of potential answers to a problem and selecting the best one. Support-Machine-Vector (SVM), K-Nearest-Neighbor (KNN), Naïve-Bayes (NB), Artificial-Neural-Network (ANN), Random-Forest (RF), and Gradient-Descent-Optimization (GDO) are just some of the ML strategies that have been utilized. Predicting Cardiovascular Disease with Intelligence, the best results may be obtained from the set of considered classification techniques, and this is where the GDO approach comes in. It has been evaluated and found to have an accuracy of 99.62 percent. The sensitivity and specificity were likewise measured at 99.65% and 98.54%, respectively. According to the findings, the proposed unique optimized algorithm has the potential to serve as a useful healthcare examination system for the timely prediction of CVD and for the study of such conditions.

Keywords

References

 
Abdalrada, A.S., Abawajy, J., Al-Quraishi, T. and Islam, S.M.S., (2022). Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study. Journal of Diabetes & Metabolic Disorders, 21(1), pp.251-261.
Ahmed, S. T., Singh, D. K., Basha, S. M., Abouel Nasr, E., Kamrani, A. K., & Aboudaif, M. K. (2021). Neural network based mental depression identification and sentiments classification technique from speech signals: A COVID-19 Focused Pandemic Study. Frontiers in public health, 9, 781827.
Ahmed, S. T., Vinoth Kumar, V., Mahesh, T. R., Narasimha Prasad, L. V., Velmurugan, A. K., Muthukumaran, V., & Niveditha, V. R. (2024). FedOPT: federated learning-based heterogeneous resource recommendation and optimization for edge computing. Soft Computing, 1-12.
Alqahtani, A., Alsubai, S., Sha, M., Vilcekova, L. and Javed, T., (2022). Cardiovascular disease detection using ensemble learning. Computational Intelligence and Neuroscience, 2022.
Ashok, K., Boddu, R., Syed, S.A., Sonawane, V.R., Dabhade, R.G. and Reddy, P.C.S., (2023). GAN Base feedback analysis system for industrial IOT networks. Automatika, 64(2), pp.259-267.
Baashar, Y., Alkawsi, G., Alhussian, H., Capretz, L.F., Alwadain, A., Alkahtani, A.A. and Almomani, M., (2022). Effectiveness of artificial intelligence models for cardiovascular disease prediction: network meta-analysis. Computational intelligence and neuroscience, 2022.
Baskar, S., Nandhini, I., Prasad, M. L., Katale, T., Sharma, N., & Reddy, P. C. S. (2023, November). An Accurate Prediction and Diagnosis of Alzheimer’s Disease using Deep Learning. In 2023 IEEE North Karnataka Subsection Flagship International Conference (NKCon) (pp. 1-7). IEEE.
Chillakuru, P., Madiajagan, M., Prashanth, K.V., Ambala, S., Shaker Reddy, P.C. and Pavan, J., (2023). Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm. Soft Computing, pp.1-11.
Dhanalakshmi, R., Bhavani, N.P.G., Raju, S.S., Shaker Reddy, P.C., Marvaluru, D., Singh, D.P. and Batu, A., (2022). Onboard pointing error detection and estimation of observation satellite data using extended kalman filter. Computational Intelligence and Neuroscience, 2022.
Dritsas, E., Alexiou, S. and Moustakas, K., (2022), April. Cardiovascular Disease Risk Prediction with Supervised Machine Learning Techniques. In ICT4AWE (pp. 315-321).
Guleria, P., Naga Srinivasu, P., Ahmed, S., Almusallam, N. and Alarfaj, F.K., (2022). XAI framework for cardiovascular disease prediction using classification techniques. Electronics, 11(24), p.4086.
Kumar, K., Pande, S.V., Kumar, T., Saini, P., Chaturvedi, A., Reddy, P.C.S. and Shah, K.B., 2023. Intelligent controller design and fault prediction using machine learning model. International Transactions on Electrical Energy Systems, 2023.
Liu, J., Dong, X., Zhao, H. and Tian, Y., (2022). Predictive classifier for cardiovascular disease based on stacking model fusion. Processes, 10(4), p.749.
LK, S. S., Ahmed, S. T., Anitha, K., & Pushpa, M. K. (2021, November). COVID-19 outbreak based coronary heart diseases (CHD) prediction using SVM and risk factor validation. In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) (pp. 1-5). IEEE.
Muthappa, K.A., Nisha, A.S.A., Shastri, R., Avasthi, V. and Reddy, P.C.S., (2023). Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs. Applied Nanoscience, pp.1-10.
Nadakinamani, R.G., Reyana, A., Kautish, S., Vibith, A.S., Gupta, Y., Abdelwahab, S.F. and Mohamed, A.W., (2022). Clinical data analysis for prediction of cardiovascular disease using machine learning techniques. Computational intelligence and neuroscience, 2022.
Reddy, P.C., Nachiyappan, S., Ramakrishna, V., Senthil, R. and Sajid Anwer, M.D., (2021). Hybrid model using scrum methodology for softwar development system. J Nucl Ene Sci Power Generat Techno, 10(9), p.2.
Reddy, P.C.S., Pradeepa, M., Venkatakiran, S., Walia, R. and Saravanan, M., (2021). Image and signal processing in the underwater environment. J Nucl Ene Sci Power Generat Techno, 10(9), p.2.
Reddy, P.C.S., Suryanarayana, G. and Yadala, S., (2022), November. Data analytics in farming: rice price prediction in Andhra Pradesh. In 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT) (pp. 1-5). IEEE.
Rustam, F., Ishaq, A., Munir, K., Almutairi, M., Aslam, N. and Ashraf, I., (2022). Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction. Diagnostics, 12(6), p.1474.
Sabitha, R., Shukla, A.P., Mehbodniya, A., Shakkeera, L. and Reddy, P.C.S., (2022). A Fuzzy Trust Evaluation of Cloud Collaboration Outlier Detection in Wireless Sensor Networks. Adhoc & Sensor Wireless Networks, 53.
Sampath, S., Parameswari, R., Prasad, M.L., Kumar, D.A., Hussain, M.M. and Reddy, P.C.S., 2023, December. Ensemble Nonlinear Machine Learning Model for Chronic Kidney Diseases Prediction. In 2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
Shaker Reddy, P.C. and Sucharitha, Y., (2022). IoT-Enabled Energy-efficient Multipath Power Control for Underwater Sensor Networks. International Journal of Sensors Wireless Communications and Control, 12(6), pp.478-494.
Shaker Reddy, P.C. and Sucharitha, Y., (2023). A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 16(5), pp.82-92.
Shanmugaraja, P., Bhardwaj, M., Mehbodniya, A., VALI, S. and Reddy, P.C.S., (2023). An Efficient Clustered M-path Sinkhole Attack Detection (MSAD) Algorithm for Wireless Sensor Networks. Adhoc & Sensor Wireless Networks, 55.
Sucharitha, Y. and Shaker Reddy, P.C., (2022). An autonomous adaptive enhancement method based on learning to optimize heterogeneous network selection. International Journal of Sensors Wireless Communications and Control, 12(7), pp.495-509.
Sucharitha, Y., Reddy, P.C.S. and Suryanarayana, G., (2023). Network Intrusion Detection of Drones Using Recurrent Neural Networks. Drone Technology: Future Trends and Practical Applications, pp.375-392.
Suneel, S., Balaram, A., Amina Begum, M., Umapathy, K., Reddy, P. C. S., & Talasila, V. (2024). Quantum mesh neural network model in precise image diagnosing. Optical and Quantum Electronics, 56(4), 559.
Tiwari, A., Chugh, A. and Sharma, A., (2022). Ensemble framework for cardiovascular disease prediction. Computers in Biology and Medicine, 146, p.105624.
Wong, D.Y., Lam, M.C., Ran, A. and Cheung, C.Y., (2022). Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions. Current Opinion in Ophthalmology, 33(5), pp.440-446.
Journal of Information Technology Management
Volume 16, Issue 1
2024
Pages 167-181

Files

Share

How to cite

Statistics