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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | ADVANCEMENTS AND RESEARCH GAPS IN HEALTHCARE PREDICTIVE ANALYTICS USING MACHINE LEARNING AND DEEP LEARNING: A COMPREHENSIVE REVIEW |
| Authors Name: | Nitin Varshney , Yash Vasoya , Harsh Nagar , Abhishek Teraiya , Mithlesh Nayak |
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IJRTI_210047
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| Published Paper Id: | IJRTI2602117 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | |
| Abstract: | Machine learning (ML) and deep learning (DL) provide a great power to healthcare predictive analytics, which create a drastic change in clinical practice by making it possible to find diseases early on, make accurate predictions about their prognoses, and customize treatment plans. Using PubMed, IEEE Xplore, and Elsevier, this research methodically evaluates 41 peer-reviewed studies from 2019 to 2022 to assess ML and DL uses in healthcare prediction. Unsupervised methods like (K-means), reinforcement learning (e.g., Q-learning) and supervised algorithms (e.g., random forests, SVM), and DL architectures (e.g., CNNs, LSTMs). Although the results show that DL is quite accurate in complex diagnostics (e.g., 100% for heart disease), instead of this it shows that there are still many problems with it, such as data heterogeneity, ethical biases, scalability, privacy, and model interpretability. Novel metrics such as the Cross-Disease Accuracy Gap (CDAG) and the Fairness Disparity (FD) provide help for the identification of five essential research gaps: limited generalizability, interpretability limitations, data imbalance, real-time limits, and fairness difficulties. To improve fairness and robustness, some suggested approaches are transfer learning, explainable AI (XAI), and federated learning. This paper provides a roadmap for academics to enhance predictive analytics while assuring clinical dependability and societal effect, using Python-generated line charts as visualizations. |
| Keywords: | Healthcare Prediction, Machine Learning, Deep Learning, Predictive Analytics, Artificial Intelligence, Medical Diagnosis. |
| Cite Article: | "ADVANCEMENTS AND RESEARCH GAPS IN HEALTHCARE PREDICTIVE ANALYTICS USING MACHINE LEARNING AND DEEP LEARNING: A COMPREHENSIVE REVIEW", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.b138-b150, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602117.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2602117
Registration ID:210047
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier):
Page No: b138-b150 Country: Rajkot, Gujrat, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602117 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602117 |
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ISSN: 2456-3315
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