Articles Open Access
Explainable Artificial Intelligence Models for Clinical Decision Support and Healthcare Recommendation Systems
AI Architect, USA
Abstract
Explainable Artificial Intelligence (XAI) has become a crucial advancement in healthcare informatics, particularly in Clinical Decision Support Systems (CDSS) and healthcare recommendation systems. While traditional machine learning and deep learning models provide high predictive accuracy in tasks such as disease diagnosis, risk prediction, and treatment recommendation, they often operate as opaque “black-box” systems. This lack of interpretability limits clinician trust, reduces adoption in real-world healthcare settings, and raises ethical concerns regarding accountability and transparency. XAI addresses these challenges by offering interpretable, transparent, and human-understandable explanations for model predictions. Techniques such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), decision trees, and attention-based neural networks are widely used to enhance explainability in clinical systems. These approaches help healthcare professionals understand which features influence predictions and how decisions are generated. The integration of XAI into CDSS improves diagnostic accuracy, supports personalized treatment planning, reduces medical errors, and enhances patient safety. Furthermore, explainable healthcare recommendation systems improve patient engagement and trust by making AI-driven suggestions more transparent. This study explores key XAI techniques, evaluates their role in clinical decision-making, reviews existing literature, and proposes a structured methodology for developing interpretable healthcare AI models. The findings emphasize that combining accuracy with explainability is essential for safe, ethical, and effective deployment of AI in healthcare environments
Keywords
DOI
PDF Download
Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 5 |
| Number | 1 |
| Pages | 7478-7486 |
| Published | 2021-01-05 |
| Issue | Vol. 5 No. 1 (2021): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Alexander, K., Hanif, M., Lee, C., Kim, E., & Helal, S. (2020). Cost-aware orchestration of applications over heterogeneous clouds. PLOS ONE, 15(2), e0228086. https://doi.org/10.1371/journal.pone.0228086
2. Balouek-Thomert, D., Gibert Renart, E., Zamani, A. R., Simonet, A., Parashar, M., & others. (2019). Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows. Journal of Parallel and Distributed Computing, 33(6), 1–15. https://doi.org/10.1177/1094342019877383
3. Bittencourt, L. F., Goldman, A., Madeira, E. R. M., Fonseca, N. L. S., & Sakellariou, R. (2019). Scheduling in distributed systems: A cloud computing perspective. arXiv preprint arXiv:1901.03270. https://arxiv.org/abs/1901.03270
4. Buyya, R., Broberg, J., & Goscinski, A. (2018). Cloud computing: Principles and paradigms. Wiley.
5. Buyya, R., Vecchiola, C., & Selvi, S. T. (2013). Mastering cloud computing. Morgan Kaufmann.
6. Ferry, N., Rossini, A., Chauvel, F., Morin, B., & Solberg, A. (2013). Model-driven provisioning of multi-cloud applications. IEEE Cloud Computing Conference, 887–894. https://doi.org/10.1109/CLOUD.2013.133
7. Grozev, N., & Buyya, R. (2014). Inter-cloud architectures and application brokering. Software: Practice and Experience, 44(3), 369–390. https://doi.org/10.1002/spe.2168
8. Helal, S., Alexander, K., Hanif, M., Lee, C., Kim, E. (2020). Multi-cloud orchestration frameworks and cost optimization. PLOS ONE. https://doi.org/10.1371/journal.pone.0228086
9. Jamshidi, P., Ahmad, A., & Pahl, C. (2013). Cloud migration research: A systematic review. IEEE Transactions on Cloud Computing, 1(2), 142–157. https://doi.org/10.1109/TCC.2013.10
10. Kavis, M. J. (2014). Architecting the cloud: Design decisions for cloud computing service models. Wiley.
11. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology. https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf
12. Mathew, A., & Nair, A., & Decouth, J. R. (2019). Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures, 5(2), 1-7.
13. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
14. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
15. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
16. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
17. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
18. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
19. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
20. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
21. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
22. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
23. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
24. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
25. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
26. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
27. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
28. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
29. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
30. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
31. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
32. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
33. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
34. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
35. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
36. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
37. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
38. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
39. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
2. Balouek-Thomert, D., Gibert Renart, E., Zamani, A. R., Simonet, A., Parashar, M., & others. (2019). Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows. Journal of Parallel and Distributed Computing, 33(6), 1–15. https://doi.org/10.1177/1094342019877383
3. Bittencourt, L. F., Goldman, A., Madeira, E. R. M., Fonseca, N. L. S., & Sakellariou, R. (2019). Scheduling in distributed systems: A cloud computing perspective. arXiv preprint arXiv:1901.03270. https://arxiv.org/abs/1901.03270
4. Buyya, R., Broberg, J., & Goscinski, A. (2018). Cloud computing: Principles and paradigms. Wiley.
5. Buyya, R., Vecchiola, C., & Selvi, S. T. (2013). Mastering cloud computing. Morgan Kaufmann.
6. Ferry, N., Rossini, A., Chauvel, F., Morin, B., & Solberg, A. (2013). Model-driven provisioning of multi-cloud applications. IEEE Cloud Computing Conference, 887–894. https://doi.org/10.1109/CLOUD.2013.133
7. Grozev, N., & Buyya, R. (2014). Inter-cloud architectures and application brokering. Software: Practice and Experience, 44(3), 369–390. https://doi.org/10.1002/spe.2168
8. Helal, S., Alexander, K., Hanif, M., Lee, C., Kim, E. (2020). Multi-cloud orchestration frameworks and cost optimization. PLOS ONE. https://doi.org/10.1371/journal.pone.0228086
9. Jamshidi, P., Ahmad, A., & Pahl, C. (2013). Cloud migration research: A systematic review. IEEE Transactions on Cloud Computing, 1(2), 142–157. https://doi.org/10.1109/TCC.2013.10
10. Kavis, M. J. (2014). Architecting the cloud: Design decisions for cloud computing service models. Wiley.
11. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology. https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf
12. Mathew, A., & Nair, A., & Decouth, J. R. (2019). Audience Behaviour Mining Using Data Analysis. International Journal of Data Structures, 5(2), 1-7.
13. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
14. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
15. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
16. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
17. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
18. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
19. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
20. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
21. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
22. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
23. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
24. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
25. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
26. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
27. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
28. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
29. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
30. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
31. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
32. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
33. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
34. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
35. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
36. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
37. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
38. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
39. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034
Statistics
76
Views
63
Downloads