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CDSS for Early Recognition of Respiratory Diseases based on AI Techniques: A Systematic Review

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Abstract

Respiratory diseases such as Asthma, COVID-19, etc., require preventive and precautionary measures. Due to the lack of medical treatment for the masses, researchers are currently focusing on clinical decision support systems (CDSS). CDSS for respiratory diseases utilizes Machine Learning (ML) techniques to classify the symptoms into a possible diseases. This approach not only grasps the attention of researchers worldwide but also assists medical doctors in the early diagnosis of the disease. In this review paper, PRISMA guidelines are used to conduct a detailed overview of the early detection of respiratory diseases using ML techniques are identified. Among various ML techniques, Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Tree (D-Tree), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), and AdaBoost are discussed. Then respiratory diseases are identified whose CDSS are available with the ML techniques and possible future direction for its improvement. Furthermore, the tools and ML techniques are compared with each other to enhance the researcher’s clarity for future use. The paper concluded with the future direction of the ML in the successful implementation of the CDSS in the field of respiratory disease.

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References

  1. Villegas, P. (1998). Viral diseases of the respiratory system. Poultry Science, 77(8), 1143–1145.

    Article  Google Scholar 

  2. Ferkol, T., & Schraufnagel, D. (2014). The global burden of respiratory disease. Annals of the American Thoracic Society, 11(3), 404–406.

    Article  Google Scholar 

  3. Pappas, G., Bosilkovski, M., Akritidis, N., Mastora, M., Krteva, L., & Tsianos, E. (2003). Brucellosis and the respiratory system. Clinical Infectious Diseases, 37(7), e95–e99.

    Article  Google Scholar 

  4. Prevention of transmission of respiratory illnesses in disaster evacuation centers. Cdc.gov, 2021. [Online]. Available: https://www.cdc.gov/disasters/disease/respiratoryic.html.

  5. Tips to keep your lungs healthy. Lung.org, 2022. [Online]. Available: https://www.lung.org/lung-health-diseases/wellness/protecting-your-lungs.

  6. Kanbay, M., Kanbay, A., & Boyacioglu, S. (2007). Helicobacter pylori infection as a possible risk factor for respiratory system disease: A review of the literature. Respiratory Medicine, 101(2), 203–209.

    Article  Google Scholar 

  7. Makker, H. (2010). Obesity and respiratory diseases. International Journal of General Medicine, 3, 335. https://doi.org/10.2147/IJGM.S11926

    Article  Google Scholar 

  8. Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. BMJ, 330(7494), 765.

    Article  Google Scholar 

  9. Wright, A., et al. (2016). Analysis of clinical decision support system malfunctions: A case series and survey. Journal of the American Medical Informatics Association, 23(6), 1068–1076.

    Article  Google Scholar 

  10. Patel, P. (2019) EHRs + clinical decision support = better healthcare. Perficient.com. [Online]. Available: https://blogs.perficient.com/2012/04/24/ehrs-clinical-decision-support-better-healthcare/.

  11. Charatan, F. (1999). Medical errors kill almost 100000 Americans a year. BMJ, 319(7224), 1519.

    Article  Google Scholar 

  12. Donaldson, L. J., Panesar, S. S., & Darzi, A. (2014). Patient-safety-related hospital deaths in England: Thematic analysis of incidents reported to a national database, 2010–2012. PLoS Medicine, 11(6), e1001667.

    Article  Google Scholar 

  13. Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17.

    Article  Google Scholar 

  14. Crevier, D. (1993). AI: The tumultuous history of the search for artificial intelligence. New York: Basic Books Inc.

    Google Scholar 

  15. Mitchell, T. M. (1997). Does machine learning really work? AI Magazine, 18(3), 11–11.

    Google Scholar 

  16. Hinton, G. (2018). Deep learning-a technology with the potential to transform health care. JAMA, 320(11), 1101–1102.

    Article  Google Scholar 

  17. Rawson, T. M., Ahmad, R., Toumazou, C., Georgiou, P., & Holmes, A. H. (2019). Artificial intelligence can improve decision-making in infection management. Nature Human Behaviour, 3(6), 543–545.

    Article  Google Scholar 

  18. Bello, A., Wiebe, N., Garg, A., & Tonelli, M. (2015). Evidence-based decision-making 2: Systematic reviews and meta-analysis. Methods in Molecular Biology, 1281, 397–416.

    Article  Google Scholar 

  19. Tawfik, G. M., et al. (2019). A step by step guide for conducting a systematic review and meta-analysis with simulation data. Tropical Medicine and Health, 47(1), 46.

    Article  Google Scholar 

  20. Liberati, A., et al. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ, 339, b2700.

    Article  Google Scholar 

  21. Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 89.

    Article  Google Scholar 

  22. PRISMA. Prisma-statement.org. [Online]. Available: http://www.prisma-statement.org/. Accessed 22 May 2022.

  23. Liz, H., Sánchez-Montañés, M., Tagarro, A., Domínguez-Rodríguez, S., Dagan, R., & Camacho, D. (2021). Ensembles of convolutional neural network models for pediatric pneumonia diagnosis. Future Generation Computer Systems, 122, 220–233.

    Article  Google Scholar 

  24. Victor Ikechukwu, A., Murali, S., Deepu, R., & Shivamurthy, R. C. (2021). ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images. Global Transitions Proceedings, 2(2), 375–381.

    Article  Google Scholar 

  25. Min Kim, H., Ko, T., Young Choi, I., & Myong, J.-P. (2021). Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image. International Journal of Medical Informatics, 158(104667), 104667.

    Google Scholar 

  26. Kavya, R., Christopher, J., Panda, S., & Lazarus, Y. B. (2021). Machine learning and XAI approaches for allergy diagnosis. Biomedical Signal Processing and Control, 69(102681), 102681.

    Article  Google Scholar 

  27. Sills, M. R., Ozkaynak, M., & Jang, H. (2021). Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning. International Journal of Medical Informatics, 151(104468), 104468.

    Article  Google Scholar 

  28. Do, Q., Son, T. C., & Chaudri, J. (2017). Classification of asthma severity and medication using TensorFlow and multilevel databases. Procedia Computer Science, 113, 344–351.

    Article  Google Scholar 

  29. Isaac, A., Nehemiah, H. K., Isaac, A., & Kannan, A. (2020). Computer-aided diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms. Computers in Biology and Medicine, 124(103940), 103940.

    Article  Google Scholar 

  30. Isaac, A., Nehemiah, H. K., Dunston, S. D., Elgin Christo, V. R., & Kannan, A. (2022). Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema. Biomedical Signal Processing and Control, 72, 103340.

    Article  Google Scholar 

  31. Madero Orozco, H., Vergara Villegas, O. O., Cruz Sánchez, V. G., de Ochoa Domínguez, H. J., & de Nandayapa Alfaro, M. J. (2015). Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. Biomedical Engineering Online, 14(1), 9.

    Article  Google Scholar 

  32. Sahin, D., Ubeyli, E. D., Ilbay, G., Sahin, M., & Yasar, A. B. (2010). Diagnosis of airway obstruction or restrictive spirometric patterns by multiclass support vector machines. Journal of Medical Systems, 34(5), 967–973.

    Article  Google Scholar 

  33. Waghmare, K., & Chatur, D. (2014). Spirometry data classification using self organizing feature map algorithm. International Journal for Research in Emerging Science and Technology, 1, 35–38.

    Google Scholar 

  34. Abadia, A. F., et al. (2022). Diagnostic accuracy and performance of artificial intelligence in detecting lung nodules in patients with complex lung disease: A noninferiority study: A noninferiority study. Journal of Thoracic Imaging, 37(3), 154–161.

    Article  Google Scholar 

  35. Li, L., et al. (2020). Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology, 296(2), E65–E71.

    Article  Google Scholar 

  36. Topalovic, M., Aerts, J.-M., Decramer, M., Troosters, T., & Janssens, W. (2017). Artificial intelligence detects lung diseases using pulmonary function tests. C47. COPD: Physiologic assessment (pp. A5678–A5678). American Thoracic Society.

    Google Scholar 

  37. Bharati, S., Podder, P., & Mondal, M. R. H. (2020). Hybrid deep learning for detecting lung diseases from X-ray images. Informatics in Medicine Unlocked, 20(100391), 100391.

    Article  Google Scholar 

  38. Zhu, J., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Li, H., & Duong, T. Q. (2020). Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS ONE, 15(7), e0236621.

    Article  Google Scholar 

  39. Topalovic, M., Das, N., Troosters, T., Decramer, M., & Janssens, W. (2017). Late breaking abstract–applying artificial intelligence on pulmonary function tests improves the diagnostic accuracy. Respiratory Function Technologists/Scientists (p. 4561). Lausanne: Eur Respiratory Soc.

    Google Scholar 

  40. Das, D. K., Chakraborty, C., & Bhattacharya, P. S. (2016). Automated screening methodology for asthma diagnosis that ensembles clinical and spirometric information. Journal of Medical and Biological Engineering, 36(3), 420–429.

    Article  Google Scholar 

  41. De Ramón Fernández, A., Ruiz Fernández, D., Gilart Iglesias, V., & Marcos Jorquera, D. (2021). Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD). International Journal of Medical Informatics, 158, 104640.

    Article  Google Scholar 

  42. Trovato, G., & Russo, M. (2021). Artificial intelligence (AI) and lung ultrasound in infectious pulmonary disease. Frontiers Medicine (Lausanne), 8, 706794.

    Article  Google Scholar 

  43. Abu-Mostafa, Y., Magdon-Ismail, M., & Lin, H. (2012). Learning from data. New York: AMLBook.

    Google Scholar 

  44. Hu, X., Luo, H., Guo, M., & Wang, J. (2022). Ecological technology evaluation model and its application based on logistic regression. Ecological Indicators, 136(108641), 108641.

    Article  Google Scholar 

  45. Meyners, M., & Hasted, A. (2022). Reply to Bi and Kuesten: ANOVA outperforms logistic regression for the analysis of CATA data. Food Quality and Preference, 95(104339), 104339.

    Article  Google Scholar 

  46. Cabero-Almenara, J., Guillén-Gámez, F. D., Ruiz-Palmero, J., & Palacios-Rodríguez, A. (2022). Teachers’ digital competence to assist students with functional diversity: Identification of factors through logistic regression methods. British Journal of Educational Technology, 53(1), 41–57.

    Article  Google Scholar 

  47. Kuncheva, L. I., & Alpaydin, E. (2007). Combining pattern classifiers: Methods and algorithms. IEEE Transactions on Neural Networks, 18(3), 964–964.

    Article  Google Scholar 

  48. Lin, J., et al. (2022). Ultrahigh energy harvesting properties in temperature-insensitive eco-friendly high-performance KNN-based textured ceramics. Journal of Materials Chemistry A. Materials for Energy and Sustainability, 10(14), 7978–7988.

    Article  Google Scholar 

  49. Zheng, T., et al. (2022). Compositionally graded KNN-based multilayer composite with excellent piezoelectric temperature stability. Advanced Materials, 34(8), e2109175.

    Article  MathSciNet  Google Scholar 

  50. Azuaje, F. (2006). Witten IH, Frank E: Data mining: Practical machine learning tools and techniques 2nd edition: San Francisco: Morgan Kaufmann publishers. Biomedical Engineering Online, 5(1), 51.

    Article  Google Scholar 

  51. Magazzino, C., Mele, M., Schneider, N., & Shahzad, U. (2022). Does export product diversification spur energy demand in the APEC region? Application of a new neural networks experiment and a decision tree model. Energy Buildings, 258(111820), 111820.

    Article  Google Scholar 

  52. Wang, K., Lu, J., Liu, A., Song, Y., Xiong, L., & Zhang, G. (2022). Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing, 491, 288–304.

    Article  Google Scholar 

  53. Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2022). A new approach to predict COVID-19 using artificial neural networks. Cyber-physical systems (pp. 139–160). Amsterdam: Elsevier.

    Chapter  Google Scholar 

  54. Hernández-Pereira, E. M., Álvarez-Estévez, D., & Moret-Bonillo, V. (2015). Automatic classification of respiratory patterns involving missing data imputation techniques. Biosystems Engineering, 138, 65–76.

    Article  Google Scholar 

  55. Widder, S., et al. (2022). Association of bacterial community types, functional microbial processes and lung disease in cystic fibrosis airways. ISME Journal, 16(4), 905–914.

    Article  Google Scholar 

  56. Uegami, W., et al. (2022). Mixture of human expertise and deep learning-developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease. Modern Pathology, 35, 1083–1091.

    Article  Google Scholar 

  57. Amini, N., & Shalbaf, A. (2022). Automatic classification of severity of COVID-19 patients using texture feature and random forest based on computed tomography images. International Journal of Imaging Systems and Technology, 32(1), 102–110.

    Article  Google Scholar 

  58. Gaur, D., & Dubey, S. K. (2022). Impact of environmental concern factors on lung diseases using machine learning. Computational intelligence in pattern recognition (pp. 719–730). Singapore: Springer.

    Chapter  Google Scholar 

  59. El-Askary, N. S., Salem, M.A.-M., & Roushdy, M. I. (2022). Features processing for random forest optimization in lung nodule localization. Expert Systems with Applications, 193(116489), 116489.

    Article  Google Scholar 

  60. Schapire, R. E. (2013). Explaining AdaBoost. Empirical inference (pp. 37–52). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  61. Vaishnaw, G. K. (2022). A method of micro pixel similarity for lung cancer diagnosis using adaboost. Algorithms for intelligent systems (pp. 75–90). Singapore: Springer.

    Google Scholar 

  62. Sevinç, E. (2022). An empowered AdaBoost algorithm implementation: A COVID-19 dataset study. Computers & Industrial Engineering, 165(107912), 107912.

    Article  Google Scholar 

  63. Venkatesh, S. P., & Raamesh, L. (2022). Predicting lung cancer survivability: A machine learning ensemble method on seer data. Research Square, 45, 4789. https://doi.org/10.21203/rs.3.rs-1490914/v1

    Article  Google Scholar 

  64. Mary, S. R., Kumar, V., Venkatesan, K. J. P., Kumar, R. S., Jagini, N. P., & Srinivas, A. (2022). Vulture-based AdaBoost-feedforward neural frame work for COVID-19 prediction and severity analysis system. Interdisciplinary Sciences, 14(2), 582–595.

    Google Scholar 

  65. Segal, G., Segev, A., Brom, A., Lifshitz, Y., Wasserstrum, Y., & Zimlichman, E. (2019). Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting. Journal of the American Medical Informatics Association, 26(12), 1560–1565.

    Article  Google Scholar 

  66. Amaral, J. L. M., Lopes, A. J., Jansen, J. M., Faria, A. C. D., & Melo, P. L. (2012). Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. Computer Methods and Programs in Biomedicine, 105(3), 183–193.

    Article  Google Scholar 

  67. Abdullah, D. M., Abdulazeez, A. M., & Sallow, A. B. (2021). Lung cancer prediction and classification based on correlation selection method using machine learning techniques. Qubahan Academic Journal, 1(2), 141–149.

    Article  Google Scholar 

  68. Bauer, Y., et al. (2021). Identifying early pulmonary arterial hypertension biomarkers in systemic sclerosis: Machine learning on proteomics from the DETECT cohort. European Respiratory Journal, 57(6), 2002591.

    Article  Google Scholar 

  69. Min, X., Yu, B., & Wang, F. (2019). Predictive modeling of the hospital readmission risk from patients’ claims data using machine learning: A case study on COPD. Science and Reports, 9(1), 2362.

    Article  Google Scholar 

  70. Karthikeyan, A., Garg, A., Vinod, P. K., & Priyakumar, U. D. (2021). Machine learning based clinical decision support system for early COVID-19 mortality prediction. Frontiers in Public Health, 9, 626697.

    Article  Google Scholar 

  71. Tiwari, S., Chanak, P., & Singh, S. K. (2022). A review of the machine learning algorithms for covid-19 case analysis. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2022.3142241

    Article  Google Scholar 

  72. Ajaz, F., Naseem, M., Sharma, S., Shabaz, M., & Dhiman, G. (2022). COVID-19: Challenges and its technological solutions using IoT. Current Medical Imaging Review, 18(2), 113–123.

    Article  Google Scholar 

  73. Sharma, M., Prakash, U., Kumari, A., & Singla, K. (2022). Early detection of covid-19 based on preliminary features using machine learning algorithms. Advances in intelligent systems and computing (pp. 391–402). Singapore: Springer.

    Google Scholar 

  74. Andrade, D. S. M., et al. (2021). Machine learning associated with respiratory oscillometry: A computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis. Biomedical Engineering Online, 20(1), 1–18. https://doi.org/10.1186/s12938-021-00865-9

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Ziauddin University, Karachi, Pakistan

    Syed Waqad Ali, Muhammad Asif, Muhammad Yousuf Irfan Zia, Munaf Rashid & Sidra Abid Syed

  2. Data Acquisition, Processing and Predictive Analytics (DAPPA) Lab, NCBC, Ziauddin University, Karachi, Pakistan

    Muhammad Asif & Munaf Rashid

  3. Department of Biomedical Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan

    Syed Waqad Ali & Sidra Abid Syed

  4. Department of Communications Engineering, University of Malaga, Malaga, Spain

    Muhammad Yousuf Irfan Zia & Enrique Nava

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Contributions

SWA: Conceptualization, Validation, Methodology, Writing- Original draft preparation, investigation. MA: Supervision, Project administration, Methodology. YIZ: Writing- Review & Editing, Validation. MR: Supervision, Project administration. SA. Resources, Formal Analysis. EN: Writing- Review & Editing, Visualization.

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Ali, S.W., Asif, M., Zia, M.Y.I. et al. CDSS for Early Recognition of Respiratory Diseases based on AI Techniques: A Systematic Review. Wireless Pers Commun 131, 739–761 (2023). https://doi.org/10.1007/s11277-023-10432-1

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