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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | AI-Driven ICU Mortality Prediction Using Machine Learning Techniques |
| Authors Name: | Sunil Sharma |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205098
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| Published Paper Id: | IJRTI2506215 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | |
| Abstract: | Intensive Care Units (ICUs) are critical environments where timely and accurate decision-making can significantly impact patient survival. Traditional scoring systems like APACHE and SOFA often fall short in adapting to complex, nonlinear patient data and real-time changes. This research introduces an AI-based mortality prediction system that leverages machine learning algorithms—including LSTM, XGBoost, and Logistic Regression—trained on large-scale ICU datasets such as MIMIC-III and eICU. The model uses a comprehensive set of features including vital signs, lab results, and patient demographics to estimate the likelihood of mortality within 24 hours of ICU admission. Evaluation metrics like AUC, accuracy, and precision demonstrate high performance, particularly with deep learning models. Additionally, SHAP-based interpretability ensures model transparency for clinical use. The system aims to support healthcare professionals by offering early warning alerts, aiding rapid intervention, and improving ICU workflow efficiency. The findings suggest that machine learning can serve as a powerful tool in reducing mortality rates and optimizing care in intensive care settings. |
| Keywords: | ICU Mortality Prediction, Machine Learning, Deep Learning, LSTM, XGBoost, MIMIC-III, eICU, Clinical Decision Support, SHAP, Healthcare AI, Patient Risk Stratification, Real-time Monitoring, Predictive Analytics, Electronic Health Records (EHR), Vital Signs |
| Cite Article: | "AI-Driven ICU Mortality Prediction Using Machine Learning Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.c119-c132, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506215.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: IJRTI2506215
Registration ID:205098
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: c119-c132 Country: Mumbai, Maharastra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506215 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506215 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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