An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population
Keywords:Logistic regression, Gaussian Naive Bayes, B-Naive Bayes, SVM, X Gradient Boosting, Decision Tree Classifier, Grid Search CV, Ada Boost Classifier, G-Boosting Classifier, Cat Boost Classifier, Extra Trees Classifier, KNN, MLP Classifier, Stochastic gradient descent, Artificial Neural Network
INTRODUCTION: Cardiovascular disease is a major concern and pressing issue faced by the healthcare sector globally. According to a survey conducted by the WHO every year, CVDs cause 17.9 million deaths worldwide. Lack of pre-prediction of CVDs is a significant factor contributing to the death of patients. Predicting CVDs is a challenging task for medical practitioners as it requires a high level of medical analysis skills and extensive knowledge.
OBJECTIVES: We believe that the improvement in the accuracy of prediction can significantly reduce the risk caused by CVDs and help medical practitioners better diagnose patients .
METHODS: In this study, We created a CVD prediction model. using a ML approach. We utilized various algorithms, including logistic regression, Gaussian Naive Baye, Bernoulli Naive Baye, SVM, KNN, optimized KNN, X Gradient Boosting, and random forest algorithms to analyze and predict CVDs.
RESULTS: Our developed prediction model achieved an accuracy of 96.7%, indicating its effectiveness in predicting CVDs. DL algorithms can also assist in identifying, classifying, and quantifying patterns of medical images, improving patient evaluation and diagnosis based on prior medical history and evaluation patterns.
CONCLUSION: Furthermore, deep learning algorithms can help in developing new drugs with minimum cost by reducing the number of clinical research trials, using prior prediction of the drug's efficacy.
Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554.
Heart disease prediction using machine learning techniques” Vijeta Sharma, Shrinkhala Yadav, Manjari Gupta 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 177-181, 2020.
Diwakar M, Tripathi A, Joshi K, Memoria M, Singh P, Kumar N. Latest trends on heart disease prediction using machine learning and image fusion. Mater Today Proc [Internet]. 2020;37(Part 2):3213–8. Available from: https://doi.org/10.1016/j.matpr.2020.09.07
Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1(6), 1-6.
Singh, A., & Kumar, R. (2020). Heart disease prediction using machine learning algorithms. 2020 International Conference on Electrical and Electronics Engineering (ICE3), 452-457.
Patel, J., Upadhyay, T., & Patel, S. (2015). Heart disease prediction using machine learning and data mining technique. Heart Disease, 7(1), 129-137.
Khourdifi, Y., Bahaj, M., & Bahaj, M. (2019). Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. International Journal of Intelligent Engineering and Systems, 12(1), 242-252.
Jagtap, A., Malewadkar, P., Baswat, O., & Rambade, H. (2019). Heart disease prediction using machine learning. International Journal of Research in Engineering, Science, and Management, 2(2), 352-355.
Ali, M. M., Paul, B. K., Ahmed, K., Bui, F. M., Quinn, J. M. W., & Moni, M. A. (2021). Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine, 136, 104672.
Jindal, H., Agrawal, S., Khera, R., Jain, R., & Nagrath, P. (2021). Heart disease prediction using machine learning algorithms. IOP Conference Series: Materials Science and Engineering, 1022(1), 012072.
Heart disease prediction using machine learning techniques” Vijeta Sharma, Shrinkhala Yadav, Manjari Gupta 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 177-181, 2020
Rajdhan, A., Agarwal, A., Sai, M., Ravi, D., & Ghuli, P. (2020). Heart disease prediction using machine learning. International Journal of Research and Technology, 9(04), 659-662.
Ramalingam, V. V., Dandapath, A., & Raja, M. K. (2018). Heart disease prediction using machine learning techniques: a survey. International Journal of Engineering & technology, 7(2.8), 684-687.
Heart disease prediction using hybrid machine learning model” M Kavitha, G Gnaneswar, R Dinesh, Y Rohith Sai, R Sai Suraj 2021 6th International Conference on Inventive Computation Technologies (ICICT), 1329-1333, 2021
Prediction of heart disease using machine learning” Aditi Gavhane, Gouthami Kokkula, Isha Pandya, Kailas Devadkar 2018 second international conference on electronics, communication and aerospace technology (ICECA), 1275-1278, 2018.
Machine learning techniques for heart disease prediction: a comparative study and analysis” Rahul Katarya, Sunit Kumar Meena Health and Technology 11 (1), 87-97, 2021
Cognitive approach for heart disease prediction using machine learning” Pranav Motarwar, Ankita Duraphe, G Suganya, M Premalatha 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 1-5, 2020
A comprehensive review on heart disease prediction using data mining and machine learning techniques” Lamido Yahaya, N David Oye, Etemi Joshua Garba American Journal of Artificial Intelligence 4 (1), 20-29, 2020
“A review on heart disease prediction using machine learning and data analytics approach” M Marimuthu, M Abinaya, KS Hariesh, K Madhankumar, V Pavithra
“Heart disease prediction using machine learning techniques” Shekharesh Barik, Sambit Mohanty, Deepankar Rout, Subhra Mohanty, Akshaya Kumar Patra, Alok Kumar Mishra.
Subramani S, Varshney N, Anand MV, Soudagar MEM, Al-Keridis LA, Upadhyay TK, Alshammari N, Saeed M, Subramanian K, Anbarasu K, Rohini K. Cardiovascular diseases prediction by machine learning incorporation with deep learning. Front Med (Lausanne). 2023 Apr 17;10:1150933. doi: 10.3389/fmed.2023.1150933. PMID: 37138750; PMCID: PMC10150633.
Barhoom, Ali M. A. ; Almasri, Abdelbaset ; Abu-Nasser, Bassem S. & Abu-Naser, Samy S. (2022). Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms. International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
Vincent Paul, S.M., Balasubramaniam, S., Panchatcharam, P. et al. Intelligent Framework for Prediction of Heart Disease using Deep Learning. Arab J Sci Eng 47, 2159–2169 (2022). https://doi.org/10.1007/s13369-021-06058-9.
Saikumar, K., Rajesh, V. A machine intelligence technique for predicting cardiovascular disease (CVD) using Radiology Dataset. Int J Syst Assur Eng Manag (2022). https://doi.org/10.1007/s13198-022-01681-7.
Bhavekar, G.S., Goswami, A.D. A hybrid model for heart disease prediction using recurrent neural network and long short term memory. Int. j. inf. tecnol. 14, 1781–1789 (2022). https://doi.org/10.1007/s41870-022-00896-y.
Ahmad, S., Asghar, M.Z., Alotaibi, F.M. et al. Diagnosis of cardiovascular disease using deep learning technique. Soft Comput 27, 8971–8990 (2023). https://doi.org/10.1007/s00500-022-07788-0.
A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms” Amin Ul Haq, Jian Ping Li, Muhammad Hammad Memon, Shah Nazir, Ruinan Sun Mobile Information Systems 2018.
Chauhan, A., Negi, P., & Chauhan, S. (2019). Heart disease prediction using machine learning algorithms: a comparative analysis. 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), 1-6.
Khan, M. A., Akhtar, N., & Ahmad, I. (2019). Heart disease prediction system using machine learning techniques. International Journal of Computer Science and Network Security, 19(3), 127-133.
Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255.
Singh, M., Sharma, S., & Singh, H. (2016). Prediction of heart disease using machine learning algorithms: a survey. International Journal of Computer Applications, 139(11), 22-25.
Alghamdi, M., Al-Mallah, M., & Keteyian, S. (2017). Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. PLoS One, 12(7), e0179805.
Masethe, H. D., & Masethe, M. A. (2014). Prediction of heart disease using classification algorithms. Proceedings of the World Congress on Engineering and Computer Science, 1, 22-24.
Sathyadevi, K., & Subramanian, R. (2011). Heart disease prediction system using supervised learning classifier algorithms. International Journal of Computer Applications, 31(10), 5-9.
Tandel, H., Vora, S., & Patel, R. (2020). Heart disease prediction using machine learning and artificial intelligence techniques: a systematic review. Journal of Ambient Intelligence and Humanized Computing, 1-15.
Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis of heart disease through neural networks ensembles. Expert Systems with Applications, 36(4), 7675-7680.
Liaw, A., & Wiener, M. (2002). Classification and regression by random Forest. R News, 2(3), 18-22.
How to Cite
Copyright (c) 2023 Manjula Mandava, Dr Surendra Reddy Vinta, Hritwik Ghosh, Irfan Sadiq Rahat
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.