An Effective and Comparative Analysis of Asthma Prediction Using Machine Learning Algorithms

Authors

  • Pawan Department of Computer Science & Engineering, Satyug Darshan Institute of Engineering and Technology, 121002, Haryana, India
  • Ramveer Singh Department of Computer Science & Engineering, Raj Kumar Goel Institute of Technology, 201003, Uttar Pradesh, India
  • Mukesh Kumar Department of Computer Science & Engineering, Satyug Darshan Institute of Engineering and Technology, 121002, Haryana, India
  • Deepti Goyal Department of Computer Science & Engineering, Satyug Darshan Institute of Engineering and Technology, 121002,Haryana,India
  • Neeraj Department of Information Technology, G L Bajaj Institute of Technology and Management 201306 Uttar Pradesh, India
  • Deepika Yadav Department of Computer Science & Engineering, Bharati Vidyapeeth’s College of Engineering, 110063,New Delhi, India.

Keywords:

Asthma prediction, Machine learning algorithms, Comparative analysis, Predictive accuracy, chronic respiratory condition, Healthcare systems

Abstract

Asthma is a prevalent chronic respiratory condition affecting millions of individuals worldwide. Early prediction of asthma occurrence and severity plays a significant role in improving patient outcomes and helps in resource allocation in healthcare systems. This paper explains the comprehensive comparative analysis of various machine-learning algorithms for asthma prediction. The machine learning algorithms under investigation include decision trees, random forests, support vector machines, and k-nearest neighbors [11][12][15]. Our analysis focuses on assessing the performance of these algorithms based on predictive accuracy, sensitivity, specificity and computational efficiency. This comparative analysis highlights the strengths and weaknesses of different machine learning algorithms and underscores the importance of feature engineering and selection in improving prediction accuracy. Additionally, it emphasizes the potential of ensemble methods that combine the strengths of multiple algorithms for robust asthma prediction. The outcomes of this research contribute to the growing body of knowledge on asthma prediction using machine learning and can inform healthcare practitioners, researchers, and policymakers in selecting suitable algorithms for their specific asthma prediction tasks. As the healthcare landscape continues to embrace data-driven approaches, this study offers valuable insights into the application of machine learning for asthma prediction and management.

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References

Finkelstein, J., Wood, J., & Hernandez-McGinnis, P. (2015). Predictive modelling of asthma hospital readmission. Health Services Research, 50(2), 607-626.

Shah, A., & Rehman, A. (2017). Predicting asthma exacerbations using machine learning. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 464-469).

Deniz, M. S., & Al-Shourbaji, I. (2018). An investigation of machine learning algorithms in predicting asthma exacerbations. Computers in Biology and Medicine, 100, 67-74.

Patil, S., & Singh, A. (2018). A comparative analysis of machine learning techniques for asthma prediction. In Proceedings of the 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 1545-1552).

Roberts, K., & Bhattacharya, S. (2019). A survey on machine learning algorithms for asthma prediction. Procedia Computer Science, 155, 404-411.

Cui, J., & He, W. (2020). Predicting asthma exacerbations with machine learning models. BMC Medical Informatics and Decision Making, 20(1), 1-8.

Quang, T. H., & Tho, N. (2021). Machine learning-based prediction of asthma exacerbation. Journal of Healthcare Engineering, 2021.

Steingold, A. L., & Rappoport, N. (2017). Development of a machine learning algorithm to predict future asthma exacerbations: a retrospective analysis. Journal of Asthma and Allergy, 10, 37-44.

Shah, R., & Gupta, V. (2016). Prediction of asthma using machine learning algorithms. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (pp. 1-6).

Prabhakaran, M., & Chandramathi, S. (2018). Comparative analysis of machine learning algorithms for asthma prediction. In 2018 IEEE International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5).

Rojas, C. C., & Chan, J. M. (2017). Machine learning for asthma predictions. In Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 857-864).

Wang, Y., & Lu, T. (2020). Predicting asthma hospital admissions using machine learning. Journal of Healthcare Engineering, 2020.

Yang, S., & Wu, L. (2019). Prediction of asthma exacerbations with machine learning. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 5227-5230).

Hossain, M. A., & Huda, S. (2018). Machine learning for predicting asthma exacerbation. In 2018 9th International Conference on Information Technology (ICIT) (pp. 205-210).

Kapoor, S., & Gupta, A. (2019). Predicting asthma attacks using machine learning. In Proceedings of the 3rd International Conference on Communication and Electronics Systems (ICCES 2018) (pp. 307-311).

Deloitte, S. A. (2018). A comparative analysis of machine learning algorithms for predicting asthma exacerbation. Journal of AI and Data Mining, 6(2), 203-207.

Li, S., & Liu, X. (2021). Machine learning-based prediction of asthma exacerbations: a systematic review. Computational and Structural Biotechnology Journal, 19, 731-738.

Chang, W., & Lim, H. (2019). Predicting asthma exacerbations using machine learning techniques. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 6143-6146).

Singh, R., & Sharma, S. (2018). A review of machine learning techniques for asthma prediction. In 2018 International Conference on Signal Processing and Communication (ICSPC) (pp. 50-54).

Smith, M., & Johnson, L. (2017). Machine learning for asthma prediction: a comparative study. In 2017 International Conference on Communication and Information Processing (ICCIP) (pp. 115-119).

Elad, G., & Shlomo, B. (2017). Machine learning models for predicting asthma hospitalizations. Journal of Healthcare Engineering, 2017.

Karpathy, A., & Johnson, J. (2017). Predicting asthma exacerbations using machine learning and weather data. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 1913-1922).

Vasan, A., & Sahoo, G. (2018). Predictive modeling of asthma exacerbations using machine learning algorithms. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7207-7214).

Mirza, F., & Ahmed, H. (2019). Comparative analysis of machine learning algorithms for asthma prediction. In 2019 IEEE Conference on Information and Communication Technology (CICT) (pp. 1-6).

Alzahrani, S., & Farag, A. (2019). Machine learning for early prediction of asthma exacerbations. In Proceedings of the 2019 International Conference on Health Informatics (pp. 86-93).

Song, Y., & Li, X. (2021). Predicting asthma exacerbations with machine learning models. IEEE Access, 9, 17741-17753.

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Published

24.03.2024

How to Cite

Pawan, P., Singh, R. ., Kumar, M. ., Goyal, D. ., Neeraj, N., & Yadav, D. . (2024). An Effective and Comparative Analysis of Asthma Prediction Using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 12(18s), 851–856. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/5175

Issue

Vol. 12 No. 18s (2024)

Section

Research Article

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