Articles Open Access
Predictive Analytics Frameworks for Early Disease Diagnosis Using Healthcare Big Data Systems
Dr. Rajendra Gode Institute of Technology and Research, Sant Gadge Baba Amravati University, Amravati, India
Abstract
Predictive analytics frameworks have emerged as a critical component in modern healthcare systems for enabling early disease diagnosis using large-scale healthcare big data. These frameworks integrate advanced data mining techniques, machine learning algorithms, artificial intelligence models, and distributed computing systems to analyze massive and heterogeneous healthcare datasets. The data sources include electronic health records (EHRs), wearable sensors, medical imaging systems, genomic data, clinical reports, and real-time patient monitoring devices. By processing and analyzing these diverse datasets, predictive analytics systems can identify hidden patterns, detect anomalies, and forecast potential disease risks before symptoms become severe. This enables healthcare professionals to take preventive actions, improve diagnostic accuracy, and enhance patient outcomes. Common predictive models include logistic regression, decision trees, random forests, support vector machines, deep neural networks, and ensemble learning methods. Big data platforms such as Hadoop and Spark provide scalable processing capabilities, while cloud computing ensures flexible storage and computational power. Despite significant advantages such as early detection, personalized medicine, and cost reduction, challenges such as data privacy, interoperability, data quality, model interpretability, and computational complexity persist. This study explores predictive analytics frameworks, literature contributions, methodologies, advantages, and limitations in healthcare big data systems for early disease diagnosis.
Keywords
Predictive AnalyticsHealthcare Big DataEarly Disease DiagnosisMachine LearningArtificial IntelligenceElectronic Health RecordsData MiningClinical Decision Support SystemsCloud ComputingDeep LearningMedical Data AnalyticsDisease Prediction ModelsHealthcare InformaticsBig Data FrameworksPrecision Medicine
DOI
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 3 |
| Number | 5 |
| Pages | 4691-4698 |
| Published | 2019-09-05 |
| Issue | Vol. 3 No. 5 (2019): International Research Journal of Innovative Engineering (IRJIE) |
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44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
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2. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques. Morgan Kaufmann.
3. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
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5. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.
6. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
7. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
8. Wang, H., & Wang, S. (2015). Big data analytics in healthcare. IEEE Access, 3, 2056–2069.
9. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(3), 1–10.
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12. Kumar, N., & Singh, S. (2013). Healthcare data mining and predictive analytics. International Journal of Computer Applications, 69(18), 1–6.
13. Sun, J., & Reddy, C. K. (2013). Big data analytics in healthcare. Proceedings of the 19th ACM SIGKDD, 1021–1029.
14. Kotsiantis, S. B. (2007). Supervised machine learning: A review. Informatica, 31(3), 249–268.
15. Lantz, B. (2013). Machine learning with R. Packt Publishing.
16. Dietterich, T. G. (2000). Ensemble methods in machine learning. Multiple Classifier Systems, 1–15.
17. Zhang, Y., & Ma, S. (2012). Ensemble machine learning: Methods and applications. Springer.
18. Pham, T., & Pham, D. (2015). Big data analytics for healthcare systems. Procedia Computer Science, 60, 143–150.
19. Nguyen, P., Tran, T., & Venkatesh, S. (2017). Deep learning for healthcare applications. IEEE Journal of Biomedical and Health Informatics, 21(1), 1–10.
20. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
21. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
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23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. 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.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
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29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
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33. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
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39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. 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.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. 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.
43. 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.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. 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.
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