Predictive Analytics for Early Disease Detection: Machine Learning Approaches using Clinical Data

References

[1] World Health Organization. (2018). Noncommunicable diseases. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases

[2] Schröder, F. H., et al. (2009). Screening and prostate-cancer mortality in a randomized European study. New England Journal of Medicine, 360(13), 1320-1328.

[3] Rajkomar, A., et al. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 1-10.

[4] Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.

[5] Zou, Q., et al. (2018). Predicting diabetes mellitus with machine learning techniques. Frontiers in Genetics, 9, 515.

[6] Alghamdi, M., et al. (2017). Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. PloS One, 12(7), e0179805.

[7] Thakur, D. (2020). Optimizing Query Performance in Distributed Databases Using Machine Learning Techniques: A Comprehensive Analysis and Implementation. IRE Journals, 3(12), 266-276.

[8] Murthy, P. & Bobba, S. (2021). AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting. IRE Journals, 5(4), 143-152.

[9] Krishna, K., Mehra, A., Sarker, M., & Mishra, L. (2023). Cloud-Based Reinforcement Learning for Autonomous Systems: Implementing Generative AI for Real-time Decision Making and Adaptation. IRE Journals, 6(8), 268-278.

[10] Thakur, D., Mehra, A., Choudhary, R., & Sarker, M. (2023). Generative AI in Software Engineering: Revolutionizing Test Case Generation and Validation Techniques. IRE Journals, 7(5), 281-293.

[11] Thakur, D. (2021). Federated Learning and Privacy-Preserving AI: Challenges and Solutions in Distributed Machine Learning. International Journal of All Research Education and Scientific Methods (IJARESM), 9(6), 3763-3771.

[12] Mehra, A. (2020). Unifying Adversarial Robustness and Interpretability in Deep Neural Networks: A Comprehensive Framework for Explainable and Secure Machine Learning Models. International Research Journal of Modernization in Engineering Technology and Science, 2(9), 1829-1838.

[13] Krishna, K. (2022). Optimizing Query Performance in Distributed NoSQL Databases through Adaptive Indexing and Data Partitioning Techniques. International Journal of Creative Research Thoughts, 10(8), e812-e823.

[14] Krishna, K. (2020). Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems. Journal of Emerging Technologies and Innovative Research, 7(4), 60-68.

[15] Murthy, P. & Mehra, A. (2021). Exploring Neuromorphic Computing for Ultra-Low Latency Transaction Processing in Edge Database Architectures. Journal of Emerging Technologies and Innovative Research, 8(1), 25-33.

[16] Krishna, K. & Thakur, D. (2021). Automated Machine Learning (AutoML) for Real-Time Data Streams: Challenges and Innovations in Online Learning Algorithms. Journal of Emerging Technologies and Innovative Research, 8(12), f730-f739

[17] Murthy, P. & Thakur, D. (2022). Cross-Layer Optimization Techniques for Enhancing Consistency and Performance in Distributed NoSQL Database. International Journal of Enhanced Research in Management & Computer Applications, 11(8), 35-41.

[18] Murthy, P. (2020). Optimizing Cloud Resource Allocation using Advanced AI Techniques: A Comparative Study of Reinforcement Learning and Genetic Algorithms in Multi-Cloud Environments. World Journal of Advanced Research and Reviews, 7(2), 359-369.

[19] Mehra, A. (2021). Uncertainty Quantification in Deep Neural Networks: Techniques and Applications in Autonomous Decision-Making Systems. World Journal of Advanced Research and Reviews, 11(3), 482-490.

[20] Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.

@article{1704160,
      author = {Praggnya Kanungo},
      title = {Predictive Analytics for Early Disease Detection: Machine Learning Approaches using Clinical Data},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {9},
      pages = {448-455},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704160.pdf},
      abstract = {Early disease detection is crucial for improving patient outcomes and reducing healthcare costs. This research explores the application of machine learning techniques for predictive analytics in early disease detection using clinical data. We evaluated multiple supervised and unsupervised machine learning algorithms on a large clinical dataset to predict the onset of five common diseases: diabetes, heart disease, stroke, lung cancer, and breast cancer. Our results demonstrate that ensemble methods like random forests and gradient boosting achieved the highest predictive performance, with AUC scores ranging from 0.82 to 0.91 across the different diseases. We also identified key clinical features that were most predictive for each disease. This study highlights the potential of machine learning approaches to leverage clinical data for early disease risk assessment and screening.},
      keywords = {Predictive Analytics, Machine Learning, Early Disease Detection, Clinical Data, Healthcare},
      month = {March},
  }