Prediction of Heart Disease using data mining Techniques Based on Hybrid CNN-light GBM Method
Keywords:
Light GBM, recursive feature elimination, hybrid CNN-Light GBM method, classification method, decision makingAbstract
In today’s situation the heart disease is one of main disease occur among the peoples who are all working in a stressful work Places. A heart disease symptom often requires electrocardiography and blood tests to find accurately; in some complicated task artificial intelligence (AI) provides fast and alternative options.But in this research for finding and predicting heart mortality, morbidity rate we introduce a effective expensive diagnosis process that isdata mining. This data mining performs a unique method for finding the best results in predicting the heart disease like data preprocessing features reduction, data conversion and data scaling are done using the standard dataset in this paper. For the preprocessing the feature scaling method is used for managing the features, variables and the independent range normalizations among the data in the data set. The next step after preprocessing is the feature selection where the features have been selected according to the target variables. For this the recursive feature elimination process has been used for selection where the scanning different feature in the data has been done. After the process of selecting the needed features among the given data’s the process of training and the testing is done for the classification of heart diseases. The work is done using the method of convolution neural networks and with the combination of the Light GBM and predicted using the combination of the above two method as hybrid CNN-Light GBM method for predicting the heart disease patient and health patient. In existing method the around 80% of accuracy has been found among the heart patient data. In this proposed system the existing method has been overcome and found the evaluation metrics performance about 97% of accuracy in finding the heart disease.
Downloads
References
Nandy, S., Adhikari, M., Balasubramanian, V., Menon, V. G., Li, X., & Zakarya, M. (2023). An intelligent heart disease prediction system based on swarm-artificial neural network. Neural Computing and Applications, 35(20), 14723-14737.
Rastogi, R., & Bansal, M. (2023). Diabetes prediction model using data mining techniques. Measurement: Sensors, 25, 100605.
Bakar, W. A. W. A., Josdi, N. L. N. B., Man, M. B., & Zuhairi, M. A. B. (2023, March). A Review: Heart Disease Prediction in Machine Learning & Deep Learning. In 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 150-155). IEEE.
Magboo, V. P. C., & Magboo, M. S. A. (2023, May). Cardiovascular disease prediction with imputation techniques and recursive feature elimination. In AIP Conference Proceedings (Vol. 2602, No. 1). AIP Publishing.
Oleiwi, Z. C., AlShemmary, E. N., & Al-augby, S. (2023). Adaptive Features Selection Technique for Efficient Heart Disease Prediction. Journal of Al-Qadisiyah for computer science and mathematics, 15(1), Page-1.
Ganie, S. M., Pramanik, P. K. D., Malik, M. B., Nayyar, A., & Kwak, K. S. (2023). An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms. Computer Systems Science & Engineering, 46(3).
MahaLakshmi, N. V., & Rout, R. K. (2023). Effective heart disease prediction using improved particle swarm optimization algorithm and ensemble classification technique. Soft Computing, 1-14.
Yang, M., Wu, Y., Yang, X. B., Liu, T., Zhang, Y., Zhuo, Y., ... & Zhang, N. (2023). Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination. Scientific Reports, 13(1), 4633.
Chandrasekhar, N., &Peddakrishna, S. (2023). Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization. Processes, 11(4), 1210.
Jain, A., Rao, A. C. S., Jain, P. K., & Hu, Y. C. (2023). Optimized levy flight model for heart disease prediction using CNN framework in big data application. Expert Systems with Applications, 223, 119859.
Gopalakrishnan, S., Sheela, M. S., Saranya, K., &Hephzipah, J. J. (2023). A Novel Deep Learning-Based Heart Disease Prediction System Using Convolutional Neural Networks (CNN) Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 516-522.
Pant, A., Rasool, A., Wadhvani, R., & Jadhav, A. (2023, January). Heart disease prediction using image segmentation Through the CNN model. In 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 374-380). IEEE.
Chen, Q., Chen, X., Chen, Y., & Song, L. (2023, July). Non-Invasive Blood Pressure Detection Method Based on CNN-LSTM-LightGBM Combination Model. In 2023 42nd Chinese Control Conference (CCC) (pp. 8695-8700). IEEE.
Ramesh¹, V., Das, M. S., & Rao¹, B. N. (2023, November). Heart Disease Detection and Prediction Using ML Algorithms in Python. In Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023) (Vol. 223, p. 347). Springer Nature.
Roy, T. S., Roy, J. K., & Mandal, N. (2023, January). Early Screening of Valvular Heart Disease Prediction using CNN-based Mobile Network. In 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) (pp. 1-8). IEEE.
Bhavekar, G. S., & Goswami, A. D. (2023). Travel-Hunt-Based Deep CNN Classifier: A Nature-Inspired Optimization Model for Heart Disease Prediction. IETE Journal of Research, 1-15.
Nissa, N., Jamwal, S., & Neshat, M. (2023). A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques.
Dissanayake, K., & Johar, M. G. M. (2023). Two-level boosting classifiers ensemble based on feature selection for heart disease prediction. Indonesian Journal of Electrical Engineering and Computer Science, 32(1), 381-391.
Bizimana, P. C., Zhang, Z., Asim, M., El-Latif, A. A. A., & Hammad, M. (2023). Learning-based techniques for heart disease prediction: a survey of models and performance metrics. Multimedia Tools and Applications, 1-55.
Shah, D., Patel, S., & Bharti, S. K. (2020). Heart disease prediction using machine learning techniques. SN Computer Science, 1, 1-6.
Yang, H., Chen, Z., Yang, H., & Tian, M. (2023). Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison. IEEE Access, 11, 23366-23380.
Güler, H., Santur, Y., & Ulaş, M. (2023). Comparison of Machine Learning Algorithms to Predict Cardiovascular Heart Disease Risk Level. International Journal of Advanced Natural Sciences and Engineering Researches (IJANSER), 7(10), 42-49.
Lv, Q. (2023, April). Hyperparameter tuning of GDBT models for prediction of heart disease. In International Conference on Electronic Information Engineering and Computer Science (EIECS 2022) (Vol. 12602, pp. 686-691). SPIE.
Qadri, A. M., Raza, A., Munir, K., & Almutairi, M. (2023). Effective Feature Engineering Technique for Heart Disease Prediction with Machine Learning. IEEE Access.
Dissanayake, K., & Johar, M. G. M. (2023). Two-level boosting classifiers ensemble based on feature selection for heart disease prediction. Indonesian Journal of Electrical Engineering and Computer Science, 32(1), 381-391.
Bemando, C., Miranda, E., &Aryuni, M. (2021, August). Machine-learning-based prediction models of coronary heart disease using naïve bayes and random forest algorithms. In 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM) (pp. 232-237). IEEE.
Sandhya, Y. (2020). Prediction of heart diseases using support vector machine. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 8(2).
Garg, A., Sharma, B., & Khan, R. (2021). Heart disease prediction using machine learning techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 1022, No. 1, p. 012046). IOP Publishing.
https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
