AI-Based Disease Prediction Models for Clinical Decision Support

Conor Judge

AI-Based Disease Prediction Models for Clinical Decision Support

Author(s)	Conor Judge
Country	Ireland
Abstract	Artificial intelligence-based disease prediction models are increasingly being applied to clinical decision support to assist healthcare professionals in identifying disease risks, evaluating patient conditions, and supporting timely clinical interventions. The growing availability of electronic health records, medical imaging, laboratory results, genomic information, wearable-device data, and other digital health information has created opportunities for developing predictive models capable of analyzing complex healthcare patterns. This study examines AI-based disease prediction models for clinical decision support, focusing on machine learning, deep learning, predictive analytics, clinical data integration, model interpretation, and healthcare decision-making. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis demonstrates that AI-based prediction models can support disease-risk assessment, early detection, patient stratification, prognosis estimation, treatment planning, and clinical monitoring. Logistic regression, decision trees, support vector machines, random forests, gradient-boosting models, neural networks, and deep-learning approaches provide different predictive capabilities depending on data characteristics and clinical objectives. However, challenges involving data quality, algorithmic bias, model interpretability, privacy, cybersecurity, interoperability, clinical validation, and generalizability can limit practical implementation. The study concludes that AI-based disease prediction can strengthen clinical decision support when predictive models are developed through rigorous validation, transparent methodologies, secure data practices, and appropriate human oversight. Future research should emphasize explainable AI, multimodal healthcare data, personalized prediction, fairness-aware modeling, federated learning, continuous model validation, and human-centered clinical decision-support systems.
Keywords	Artificial Intelligence, Disease Prediction, Clinical Decision Support, Machine Learning, Deep Learning, Predictive Analytics, Healthcare Informatics, Medical Data, Clinical Decision-Making, Healthcare AI
Field	Engineering
Published In	Volume 5, Issue 2, March-April 2023
Published On	2023-04-04

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American International Journal of Advanced Research and Development

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AI-Based Disease Prediction Models for Clinical Decision Support

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