• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 15, Pages: 1107-1117

Original Article

Early Detection and Classification of Heart Diseases by Employing IFCMML and 2L-C Model with I-GA Machine Learning Methods

Received Date:08 March 2023, Accepted Date:17 March 2023, Published Date:17 April 2023


Objectives: To identify a new method for early detection and classification of Heart Diseases (HD); to improve the accuracy of the results by employing I-FCMML (Improved Fuzzy C-Means Machine Learning) and 2L-C (Two-Level Classifier) models along with the I-GA (Improved Genetic Algorithm) methods. Methods: The I-FCMML algorithm is utilized for feature selection and extraction. Machine learning techniques such as Ensembled Random Forest method (ERFM) and Robust Gradient Boost Method (RGBT) are employed to predict the likelihood of HD and 2L-C, I-GA is used to classify and detect the HD at a premature stage based on features like age, gender, blood pressure, etc. IFCMML with 2L-C & I-GA extracts all the features from the dataset (Cleveland HD dataset from the Cleveland Clinic Foundation, Ohio, USA) which includes 303 observations, 14 features and selects the suitable function to perform disease classification and detection with high accuracy. To evaluate the performance of the proposed method, MATLAB is used for implementation. The results are compared with existing algorithms such as 3P-ANN, ANN-FAHP, ADWFS, EDSS, and FE-PCA. Findings: Early HD detection and classification is achieved with 96.02% accuracy, 95.80% sensitivity, 94.76% specificity, 95% precision, 94% recall, 0.90 True Positive, 0.87 True Negative, and 94.13% F-Score to detect and classify the HD in a robust manner, which is comparatively high than the existing methods. Novelty: According to the findings of the comprehensive study, the proposed new method I-FCMML with 2L-C & I-GA has the potential to provide accurate and competent detection and classification of HD at an early stage, which could help for timely treatment and management of HD patients, and it also outperforms the existing methods such as 3P-ANN, ANNFAHP, ADWFS, EDSS, and FE-PCA.

Keywords: Heart Disease Detection; Classification Algorithm; Genetic Algorithm; Fuzzy C-Means ML; Image Processing


  1. Nagavelli U, Samanta D, Chakraborty P. Machine Learning Technology-Based Heart Disease Detection Models. Journal of Healthcare Engineering. 2022;2022:1–9. Available from: https://doi.org/10.1155/2022/7351061
  2. Chen W, Sun, Qiang, Chen, Xiaomin, Xie, et al. Deep Learning Methods for Heart Sounds Classification: A Systematic Review. Entropy. 2021;23:667. Available from: https://doi.org/10.3390/e23060667
  3. Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Computational Intelligence and Neuroscience. 2021;2021:1–11. Available from: https://doi.org/10.1155/2021/8387680
  4. Saboor A, Usman M, Ali S, Samad A, Abrar MF, Ullah N. A Method for Improving Prediction of Human Heart Disease Using Machine Learning Algorithms. Mobile Information Systems. 2022;2022:1–9. Available from: https://doi.org/10.1155/2022/1410169
  5. Albahr A, Albahar M, Thanoon M, Binsawad M. Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer. Computational Intelligence and Neuroscience. 2021;2021:1–10. Available from: https://doi.org/10.1155/2021/8628335
  6. Yilmaz R, Yağin FH. Early Detection of Coronary Heart Disease Based on Machine Learning Methods. Medical Records. 2022;2022(1):1–6. Available from: https://doi.org/10.37990/medr.1011924
  7. González-Corrales R, García-García C, Casal-Moro R, Berlanga-Llavori R, Martínez-Álvarez RP, García-Sáez G. A machine learning-based approach to predict cardiovascular disease risk using bioelectrical impedance analysis and clinical information. Journal of Healthcare Engineering. 2021. Available from: https://doi.org/10.1155/2021/6623843
  8. Ghwanmeh S, Mohammad A, Al-Ibrahim A. Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis. Journal of Intelligent Learning Systems and Applications. 2013;05(03):176–183. Available from: https://doi.org/10.4236/jilsa.2013.53019
  9. Manimurugan S, Almutairi S, Aborokbah MM, Narmatha C, Ganesan S, Chilamkurti N, et al. Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence. Sensors. 2022;22(2):476. Available from: https://doi.org/10.3390/s22020476
  10. Arooj S, Rehman SU, Imran A, Almuhaimeed A, Alzahrani AK, Alzahrani AK. A Deep Convolutional Neural Network for the Early Detection of Heart Disease. Biomedicines. 2022;10(11):2796. Available from: https://doi.org/10.3390/biomedicines10112796
  11. Al-Shabi M, Abuhamdah A. Using deep learning to detecting abnormal behavior in internet of things. International Journal of Electrical and Computer Engineering (IJECE). 2022;12(2):2108. Available from: http://doi.org/10.11591/ijece.v12i2.pp2108-2120
  12. Wafa M, Ansari SE, Humphries AK, Naveed OJ, Khan DA, Khan E, et al. Effect of Coronary Artery Disease risk SNPs on serum cytokine levels and cytokine imbalance in Premature Coronary Artery Disease. Cytokine. 2019;122. Available from: https://doi.org/10.1016/j.cyto.2017.05.013
  13. Kaur J, Khehra BS. Fuzzy Logic and Hybrid based Approaches for the Risk of Heart Disease Detection: State-of-the-Art Review. Journal of The Institution of Engineers (India): Series B. 2022;103(2):681–697. Available from: https://doi.org/10.1007/s40031-021-00644-z
  14. Mohan S, Thirumalai C, Srivastava G. Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques. IEEE Access. 2019;7:81542–81554. Available from: https://doi.org/10.1109/ACCESS.2019.2923707
  15. Sharma N, Singla A. Early detection of heart disease using 3P ANN classifier. IEEE Computing Methodologies and Communication. 2020;4(6):210–215. Available from: https://doi.org/10.1109/ICCMC51329.2020.9190445
  16. Li JP, Haq AU, Din SU, Khan J, Khan A, Saboor A. Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare. IEEE Access. 2020;8(1):107562–107582. Available from: https://doi.org/10.1109/ACCESS.2020.3001149
  17. Kumar PS, Kumar N, Khanna N. Detection of Coronary Heart Disease Using Advanced Data Mining and Feature Selection Techniques. International Journal of Innovative Technology and Exploring Engineering. 2019;8(5S2):636–640. Available from: https://doi.org/10.35940/ijitee.K8733.0985S219
  18. Saadon HD, Bakar AM, Sulaiman MY. Early Detection of Heart Diseases Using Feature Extraction and Principal Component Analysis. Journal of Healthcare Engineering. 2021;p. 1–8. Available from: https://doi.org/10.1155/2021/6615692
  19. Bhatt CM, Patel P, Ghetia T, Mazzeo PL. Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms. 2023;16(2):88. Available from: https://doi.org/10.3390/a16020088


© 2023 Lakshmi & Sujatha. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee


Subscribe now for latest articles and news.