Hybrid Machine Learning Model for Chronic Disease Prediction
Keywords:
Chronical Disease, Machine Learning, Hybrid Model, Logistic Regression, Random ForestAbstract
The previously suggested chronic disease prediction techniques are incapable of acquiring efficiency in feature extraction, outlier removal and classification. This research work is conducted to tackle the limitations of these methods. After eliminating the existing drawbacks, the accuracy to predict the chronic disease is augmented consequently. Therefore, the fundamental emphasize is on predicting the disease on the basis of economic and social data, and analyzing the trends of chronic diseases depending upon the epidemiological data. This work suggests a hybrid framework in which Random Forest (RF) is integrated with Logistic Regression (LR). The initial algorithm is implemented for extracting the features, and the latter one is exploited for classifying the diseases. Logistic Regression algorithm makes the deployment of extracted features as input to classify the data. Python is executed to simulate the suggested framework. Various metrics, namely accuracy, precision and recall are utilized to analyze the results.
Downloads
References
M. U. Khan, S. Zuriat-e-Zehra Ali, A. Ishtiaq, K. Habib, T. Gul and A. Samer, "Classification of Multi-Class Cardiovascular Disorders using Ensemble Classifier and Impulsive Domain Analysis," 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), 2021, pp. 1-8
N. Rajesh, A. C. Ramachandra and A. Prathibha, "Detection and Identification of Irregularities in Human Heart Rate," 2021 International Conference on Intelligent Technologies (CONIT), 2021, pp. 1-5
S. M. Rayavarapu, D. Bikshapathi, S. L. Sabat and J. S. A. E. Fouda, "FPGA implementation of Ordinal Pattern Analysis algorithm for Early Detection of Cardiovascular diseases," 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019, pp. 1-4
R. Banerjee, S. Bhattacharya, S. Bandyopadhyay, A. Pal and K. M. Mandana, "Non-Invasive Detection of Coronary Artery Disease Based on Clinical Information and Cardiovascular Signals: A Two-Stage Classification Approach," 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 2018, pp. 205-210
B. D. Sekar, M. Chui Dong, J. Shi and X. Y. Hu, "Fused Hierarchical Neural Networks for Cardiovascular Disease Diagnosis," in IEEE Sensors Journal, vol. 12, no. 3, pp. 644-650, March 2012
O. Terrada, A. Raihani, O. Bouattane and B. Cherradi, "Fuzzy cardiovascular diagnosis system using clinical data," 2018 4th International Conference on Optimization and Applications (ICOA), 2018, pp. 1-4
A. Lakshmanarao, A. Srisaila and T. S. R. Kiran, "Heart Disease Prediction using Feature Selection and Ensemble Learning Techniques," Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), vol. 23, no. 2, pp. 980-988, 2021
V. Gupta, V. Aggarwal, S. Gupta, N. Sharma, K. Sharma and N. Sharma, "Visualization and Prediction of Heart Diseases Using Data Science Framework," Second International Conference on Electronics and Sustainable Communication Systems (ICESC), vol. 1, no. 2, pp. 1199-1206, 2021
T. Santhanam and E. P. Ephzibah, “Heart Disease Prediction Using Hybrid Genetic Fuzzy Model”, Indian Journal of Science and Technology, vol. 8, no. 23, pp: 797–803, 2015
G. Purusothaman and P. Krishnakumari, “A Survey of Data Mining Techniques on Risk Prediction: Heart Disease”, Indian Journal of Science and Technology, vol. 12, no. 3, pp. 124-131, 2015
R. Katarya and P. Srinivas, "Predicting Heart Disease at Early Stages using Machine Learning: A Survey," International Conference on Electronics and Sustainable Communication Systems (ICESC), vol. 1, no. 56, pp. 758–766, 2020
V. Sharma, A. Rasool and G. Hajela, “Prediction of Heart disease using DNN”, Second International Conference on Inventive Research in Computing Applications (ICIRCA), vol. 10, no. 7, pp. 554-562, 2020
Victor Chang, Vallabhanent Rupa Bhavani, MA Hossain, “An artificial intelligence model for heart disease detection using machine learning algorithms”, Journal of Healthcare Analytics, vol. 2, no. 5, pp. 342-350, 2022
A. Rahim, Y. Rasheed, F. Azam, M. W. Anwar, M. A. Rahim and A. W. Muzaffar, "An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases," in IEEE Access, vol. 9, pp. 106575-106588, 2021
H. D. Park, Y. Han and J. H. Choi, "Frequency-Aware Attention based LSTM Networks for Cardiovascular Disease," 2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018, pp. 1503-1505
Y. An, N. Huang, X. Chen, F. Wu and J. Wang, "High-Risk Prediction of Cardiovascular Diseases via Attention-Based Deep Neural Networks," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 3, pp. 1093-1105, 1 May-June 2021
W. Zeng, X. Wang, K. Xu, Y. Zhang and H. Fu, "Prediction of cardiovascular disease survival based on artificial neural network," 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), 2021, pp. 219-224
D. M. R., S. Kuwelkar and R. Sivakumar, “An hybrid technique for optimized clustering of EHR using binary particle swarm and constrained optimization for better performance in prediction of cardiovascular diseases”, Measurement: Sensors, vol. 9, no. 4, pp. 1376-1390, 17 December 2022
K. Junwei, H. Yang, L. Junjiang and Y. Zhijun, "Dynamic prediction of cardiovascular disease using improved LSTM," in International Journal of Crowd Science, vol. 3, no. 1, pp. 14-25, April 2019
V. S. Dehnavi and M. Shafiee, "The risk prediction of heart disease by using neuro-fuzzy and improved GOA," 2020 11th International Conference on Information and Knowledge Technology (IKT), 2020, pp. 127-131
P. Theerthagiri, “Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique”, Intelligent Systems with Applications, vol. 8, no. 5, pp. 1079-1094, 6 September 2022
R. Li, S. Yang and W. Xie, "Cardiovascular Disease Prediction Model Based on Logistic Regression and Euclidean Distance," 2021 4th International Conferenc on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 2021, pp. 711-715
A. Elbadry and S. Eldawlatly, "Majority-Vote Over Multiple ECG Segments for Risk Assessment (MOMESRA): A Machine Learning Approach for Predicting Cardiovascular Events," 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE), 2021, pp. 1-6
N. S. Rajjliwal and G. Chetty, "Deep Learning Based Decision Support Framework for Cardiovascular Disease Prediction," 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2021, pp. 1-1
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.