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A Machine Learning Approach for Accurate Diagnosis of Heart Disease: A Comparative Study

Yıl 2025, Cilt: 6 Sayı: 1, 13 - 22, 29.06.2025

Ahmed Shahab Ahmed Al-slemani

https://doi.org/10.46572/naturengs.1607104

Öz

Heart diseases are leading determinants of worldwide morbidity indices, underscoring the critical importance of accurate diagnostic assessments in facilitating timely therapeutic interventions and optimizing clinical outcomes. Traditional methods of diagnosis rely on clinical evaluations and laboratory tests, which may be subjective and prone to errors. In the present study, we suggest implementing a machine learning methodology to construct a prognostic framework for heart disease diagnosis by integrating demographic details, medical records, and laboratory information.
In this research, in order to design an intelligent monitoring system for cardiac disease, to increase the accuracy of diagnosis and also reduce the classification error rate, as an innovative aspect of the research, we used a recurrent neural network LSTM with the optimal neuron value of its hidden layer obtained using the particle optimization algorithm. In this study, after preprocessing by normalizing the data and removing missing data, we reduced the number of unimportant features using principal component analysis in the next step so that we could classify the data more accurately using the recurrent neural network. In this research, we attained a precision level of 99.3%, surpassing the proposed technique by 1% when contrasted with the conventional method outlined in the foundational article.

Anahtar Kelimeler

Machine Learning Accurate Diagnosis Heart Disease Comparative Study

Kaynakça

  • Abdulbaqi, A. S., Obaid, A. J., & Alazawi, S. A. H. (2021). A smart system for health caregiver based on IoMT: Toward tele-health caregiving. International Journal of Online & Biomedical Engineering, 17(7).
  • Rajpurkar, M., & Kazar, O. (2021). A cloud-IoT health monitoring system based on smart agent for cardiovascular patients. In 2021 International Conference on Information Technology (ICIT) (pp. 1–6). IEEE.
  • Wang, M. B. (2019). Diagnosis of cardiovascular diseases based on machine learning using ultrasound images. Wireless Communications and Mobile Computing.
  • Alkayyali, Z. K., Idris, S. A. B., & Abu-Naser, S. S. (2023). A systematic literature review of deep and machine learning algorithms in cardiovascular diseases diagnosis. Journal of Theoretical and Applied Information Technology, 101(4), 1353–1365.
  • Khan, A., Qureshi, M., Daniyal, M., & Tawiah, K. (2023). A novel study on machine learning algorithm-based cardiovascular disease prediction. Health & Social Care in the Community, 2023, Article ID 1406060. https://doi.org/10.1155/2023/1406060
  • Shen, N. A., Abdelaliem, S. M. F., & Malki, A. et al. (2018). Classification based on deep learning of cardiac arrhythmias using ECG signals. Journal of Big Data, 10, 144. https://doi.org/10.1186/s40537-023-00817-1
  • Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021). A review on the role of machine learning in enabling IoT-based healthcare applications. IEEE Access, 9, 38859–38890.
  • Deperlioglu, O., Kose, U., Gupta, D., Khanna, A., & Sangaiah, A. K. (2020). Diagnosis of heart diseases by a secure Internet of Health Things system based on autoencoder deep neural network. Computer Communications, 162, 31–50.
  • Islam, M. N., Raiyan, K. R., Mitra, S., Mannan, M. R., Tasnim, T., Putul, A. O., & Mandol, A. B. (2023). Predictis: An IoT and machine learning-based system to predict risk level of cardiovascular diseases. BMC Health Services Research, 23(1), 171.
  • Kavitha, D., & Ravikumar, S. (2021). IoT and context-aware learning-based optimal neural network model for real-time health monitoring. Transactions on Emerging Telecommunications Technologies, 32(1), e4132.
  • Liu, J., Zhu, Z., Zhou, Y., Wang, N., Dai, G., Liu, Q., & Zhou, J. (2021). BioAIP: A reconfigurable biomedical AI processor with adaptive learning for versatile intelligent health monitoring. In 2021 IEEE International Solid-State Circuits Conference (ISSCC) (pp. 62–64).
  • Anjum, N., Siddiqua, C. U., Haider, M., Ferdus, Z., Raju, M. A. H., Imam, T., & Rahman, M. R. (2024). Improving cardiovascular disease prediction through comparative analysis of machine learning models. Journal of Computer Science and Technology Studies, 6(2), 62–70. https://doi.org/10.32996/jcsts.2024.6.2.7
  • Ogunpola, A., Saeed, F., Basurra, S., Albarrak, A. M., & Qasem, S. N. (2024). Machine learning-based predictive models for detection of cardiovascular diseases. Diagnostics, 14(2), 144. https://doi.org/10.3390/diagnostics14020144
  • Panchatcharam, P., Manogaran, G., & Varadharajan, R. (2018). A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design Automation for Embedded Systems, 22(3), 225–242.
  • Pardeshi, D., Rawat, P., Raj, A., Gadbail, P., Solanki, R. K., & Bhaladhare, P. R. (2023). Efficient approach for detecting cardiovascular disease using machine learning. International Journal of Aquatic Science, 14(1), 308–321.
  • Premalatha, G., & Bai, V. T. (2022). Design and implementation of intelligent patient in-house monitoring system based on efficient XGBoost-CNN approach. Cognitive Neurodynamics, 1–15.
  • Baral, S., Satpathy, S., Pati, D. P., Mishra, P., & Pattnaik, L. (2024). A literature review for detection and projection of cardiovascular disease using machine learning. EAI Endorsed Transactions on Internet of Things, 10.
  • Sahoo, A. K., Pradhan, C., & Das, H. (2020). Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. In Nature Inspired Computing for Data Science (pp. 201–212). Springer, Cham.
  • Saikumar, K., & Rajesh, V. (2024). A machine intelligence technique for predicting cardiovascular disease (CVD) using radiology dataset. International Journal of Systems Assurance Engineering and Management, 15, 135–151. https://doi.org/10.1007/s13198-022-01681-7
  • Sandhiya, S., & Palani, U. (2020). An effective disease prediction system using incremental feature selection and temporal convolutional neural network. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5547–5560.
  • Rao, G. S., & Muneeswari, G. (2024). A review: Machine learning and data mining approaches for cardiovascular disease diagnosis and prediction. EAI Endorsed Transactions on Pervasive Health Technologies, 10. https://publications.eai.eu/index.php/phat/article/view/5411
  • Taylan, O., Alkabaa, A. S., Alqabbaa, H. S., Pamukçu, E., & Leiva, V. (2023). Early prediction in classification of cardiovascular diseases with machine learning, neuro-fuzzy, and statistical methods. Biology, 12(1), 117. https://doi.org/10.3390/biology12010117
  • Huang, J.-D., Wang, J., Ramsey, E., Leavey, G., Chico, T. J. A., & Condell, J. (2022). Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: A review. Sensors, 22, 8002. https://doi.org/10.3390/s22208002
  • Liu, X. Q., Jiang, T. T., Wang, M. Y., Liu, W. T., Huang, Y., Huang, Y. L., et al. (2019). A machine-learning approach to predict the risk of cardiovascular disease using electronic health information. Frontiers in Immunology, 12, 796383. https://doi.org/10.3389/fimmu.2021.796383
  • Maini, E., Venkateswarlu, B., & Gupta, A. (2018). Applying machine learning algorithms to develop a universal cardiovascular disease prediction system. International Conference on Intelligent Data Communication Technologies and Internet of Things (ICIDCT), 627–632.
  • AbdulSaboor, M. U., Ali, S., Ali, S., Abrar, M. F., & Ullah, N. (2022). A method for improving prediction of human heart disease using machine learning algorithms. Mobile Information Systems, 2022, Article 1410169.
  • Modepalli, K., Gnaneswar, G., Dinesh, R., Sai, Y., & Suraj, R. (2021). Heart disease prediction using a hybrid machine learning model. Proceedings of the 2021 International Conference on Information Communication Technology (pp. 1329–1333). https://doi.org/10.1109/ICICT50816.2021.9358597
  • Kataria, R., & Meena, S. K. (2021). Machine learning techniques for heart disease prediction: A comparative study and analysis. Health Technology, 11, 87–97.
  • Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1, 345. https://doi.org/10.1007/s42979-020-00365-y
  • Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational Intelligence and Neuroscience, 2021, Article 8387680.
  • Ashish, L., Kumar, S., & Yeligeti, S. (2021). Ischemic heart disease detection using support vector machine and extreme gradient boosting method. Materials Today Proceedings, 6, 5873–5878.
  • Al-slemani, A. S. A., & Zengin, A. (2023). A New Surveillance and Security Alert System Based on Real-Time Motion Detection. Journal of Smart Systems Research, 4(1), 31-47. https://doi.org/10.58769/joinssr.1262853
  • Al-slemani, A. S. A., & Abubakr, G. (2024). Adaptive Landmine Detection and Recognition in Complex Environments using YOLOv8 Architectures. Journal of Smart Systems Research, 5(2), 110-120. https://doi.org/10.58769/joinssr.1542886

Yıl 2025, Cilt: 6 Sayı: 1, 13 - 22, 29.06.2025

Ahmed Shahab Ahmed Al-slemani

https://doi.org/10.46572/naturengs.1607104

Öz

Kaynakça

  • Abdulbaqi, A. S., Obaid, A. J., & Alazawi, S. A. H. (2021). A smart system for health caregiver based on IoMT: Toward tele-health caregiving. International Journal of Online & Biomedical Engineering, 17(7).
  • Rajpurkar, M., & Kazar, O. (2021). A cloud-IoT health monitoring system based on smart agent for cardiovascular patients. In 2021 International Conference on Information Technology (ICIT) (pp. 1–6). IEEE.
  • Wang, M. B. (2019). Diagnosis of cardiovascular diseases based on machine learning using ultrasound images. Wireless Communications and Mobile Computing.
  • Alkayyali, Z. K., Idris, S. A. B., & Abu-Naser, S. S. (2023). A systematic literature review of deep and machine learning algorithms in cardiovascular diseases diagnosis. Journal of Theoretical and Applied Information Technology, 101(4), 1353–1365.
  • Khan, A., Qureshi, M., Daniyal, M., & Tawiah, K. (2023). A novel study on machine learning algorithm-based cardiovascular disease prediction. Health & Social Care in the Community, 2023, Article ID 1406060. https://doi.org/10.1155/2023/1406060
  • Shen, N. A., Abdelaliem, S. M. F., & Malki, A. et al. (2018). Classification based on deep learning of cardiac arrhythmias using ECG signals. Journal of Big Data, 10, 144. https://doi.org/10.1186/s40537-023-00817-1
  • Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021). A review on the role of machine learning in enabling IoT-based healthcare applications. IEEE Access, 9, 38859–38890.
  • Deperlioglu, O., Kose, U., Gupta, D., Khanna, A., & Sangaiah, A. K. (2020). Diagnosis of heart diseases by a secure Internet of Health Things system based on autoencoder deep neural network. Computer Communications, 162, 31–50.
  • Islam, M. N., Raiyan, K. R., Mitra, S., Mannan, M. R., Tasnim, T., Putul, A. O., & Mandol, A. B. (2023). Predictis: An IoT and machine learning-based system to predict risk level of cardiovascular diseases. BMC Health Services Research, 23(1), 171.
  • Kavitha, D., & Ravikumar, S. (2021). IoT and context-aware learning-based optimal neural network model for real-time health monitoring. Transactions on Emerging Telecommunications Technologies, 32(1), e4132.
  • Liu, J., Zhu, Z., Zhou, Y., Wang, N., Dai, G., Liu, Q., & Zhou, J. (2021). BioAIP: A reconfigurable biomedical AI processor with adaptive learning for versatile intelligent health monitoring. In 2021 IEEE International Solid-State Circuits Conference (ISSCC) (pp. 62–64).
  • Anjum, N., Siddiqua, C. U., Haider, M., Ferdus, Z., Raju, M. A. H., Imam, T., & Rahman, M. R. (2024). Improving cardiovascular disease prediction through comparative analysis of machine learning models. Journal of Computer Science and Technology Studies, 6(2), 62–70. https://doi.org/10.32996/jcsts.2024.6.2.7
  • Ogunpola, A., Saeed, F., Basurra, S., Albarrak, A. M., & Qasem, S. N. (2024). Machine learning-based predictive models for detection of cardiovascular diseases. Diagnostics, 14(2), 144. https://doi.org/10.3390/diagnostics14020144
  • Panchatcharam, P., Manogaran, G., & Varadharajan, R. (2018). A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease. Design Automation for Embedded Systems, 22(3), 225–242.
  • Pardeshi, D., Rawat, P., Raj, A., Gadbail, P., Solanki, R. K., & Bhaladhare, P. R. (2023). Efficient approach for detecting cardiovascular disease using machine learning. International Journal of Aquatic Science, 14(1), 308–321.
  • Premalatha, G., & Bai, V. T. (2022). Design and implementation of intelligent patient in-house monitoring system based on efficient XGBoost-CNN approach. Cognitive Neurodynamics, 1–15.
  • Baral, S., Satpathy, S., Pati, D. P., Mishra, P., & Pattnaik, L. (2024). A literature review for detection and projection of cardiovascular disease using machine learning. EAI Endorsed Transactions on Internet of Things, 10.
  • Sahoo, A. K., Pradhan, C., & Das, H. (2020). Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. In Nature Inspired Computing for Data Science (pp. 201–212). Springer, Cham.
  • Saikumar, K., & Rajesh, V. (2024). A machine intelligence technique for predicting cardiovascular disease (CVD) using radiology dataset. International Journal of Systems Assurance Engineering and Management, 15, 135–151. https://doi.org/10.1007/s13198-022-01681-7
  • Sandhiya, S., & Palani, U. (2020). An effective disease prediction system using incremental feature selection and temporal convolutional neural network. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5547–5560.
  • Rao, G. S., & Muneeswari, G. (2024). A review: Machine learning and data mining approaches for cardiovascular disease diagnosis and prediction. EAI Endorsed Transactions on Pervasive Health Technologies, 10. https://publications.eai.eu/index.php/phat/article/view/5411
  • Taylan, O., Alkabaa, A. S., Alqabbaa, H. S., Pamukçu, E., & Leiva, V. (2023). Early prediction in classification of cardiovascular diseases with machine learning, neuro-fuzzy, and statistical methods. Biology, 12(1), 117. https://doi.org/10.3390/biology12010117
  • Huang, J.-D., Wang, J., Ramsey, E., Leavey, G., Chico, T. J. A., & Condell, J. (2022). Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: A review. Sensors, 22, 8002. https://doi.org/10.3390/s22208002
  • Liu, X. Q., Jiang, T. T., Wang, M. Y., Liu, W. T., Huang, Y., Huang, Y. L., et al. (2019). A machine-learning approach to predict the risk of cardiovascular disease using electronic health information. Frontiers in Immunology, 12, 796383. https://doi.org/10.3389/fimmu.2021.796383
  • Maini, E., Venkateswarlu, B., & Gupta, A. (2018). Applying machine learning algorithms to develop a universal cardiovascular disease prediction system. International Conference on Intelligent Data Communication Technologies and Internet of Things (ICIDCT), 627–632.
  • AbdulSaboor, M. U., Ali, S., Ali, S., Abrar, M. F., & Ullah, N. (2022). A method for improving prediction of human heart disease using machine learning algorithms. Mobile Information Systems, 2022, Article 1410169.
  • Modepalli, K., Gnaneswar, G., Dinesh, R., Sai, Y., & Suraj, R. (2021). Heart disease prediction using a hybrid machine learning model. Proceedings of the 2021 International Conference on Information Communication Technology (pp. 1329–1333). https://doi.org/10.1109/ICICT50816.2021.9358597
  • Kataria, R., & Meena, S. K. (2021). Machine learning techniques for heart disease prediction: A comparative study and analysis. Health Technology, 11, 87–97.
  • Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1, 345. https://doi.org/10.1007/s42979-020-00365-y
  • Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational Intelligence and Neuroscience, 2021, Article 8387680.
  • Ashish, L., Kumar, S., & Yeligeti, S. (2021). Ischemic heart disease detection using support vector machine and extreme gradient boosting method. Materials Today Proceedings, 6, 5873–5878.
  • Al-slemani, A. S. A., & Zengin, A. (2023). A New Surveillance and Security Alert System Based on Real-Time Motion Detection. Journal of Smart Systems Research, 4(1), 31-47. https://doi.org/10.58769/joinssr.1262853
  • Al-slemani, A. S. A., & Abubakr, G. (2024). Adaptive Landmine Detection and Recognition in Complex Environments using YOLOv8 Architectures. Journal of Smart Systems Research, 5(2), 110-120. https://doi.org/10.58769/joinssr.1542886
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Sistem Yazılımı, Bilgisayar Yazılımı
Bölüm Research Articles
Yazarlar

Ahmed Shahab Ahmed Al-slemani 0000-0002-5668-9922

Erken Görünüm Tarihi 29 Haziran 2025
Yayımlanma Tarihi 29 Haziran 2025
Gönderilme Tarihi 25 Aralık 2024
Kabul Tarihi 29 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 1

Kaynak Göster

APA Al-slemani, A. S. A. (2025). A Machine Learning Approach for Accurate Diagnosis of Heart Disease: A Comparative Study. NATURENGS, 6(1), 13-22. https://doi.org/10.46572/naturengs.1607104
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