Intelligent Prediction of Human Health Risks Based on Medical History: A Review

Kamal, Mais Irreem A; Ramo, Fawziya M.

doi:10.33899/csmj.2024.147703.1113

	Intelligent Prediction of Human Health Risks Based on Medical History: A Review
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 5, Volume 18, Issue 2, December 2024, Pages 33-45 PDF (603.33 K)
Document Type: Review Paper
DOI: 10.33899/csmj.2024.147703.1113
Authors
Mais Irreem A kamal^* ; Fawziya M. Ramo
Department of Computer Science, College of Computer Science and Mathematics, University of Mosul, Iraq
Abstract
The main access to modern healthcare using artificial technologies is related to the medical topic and prediction of risks to human health. Enhance patient medical care using intelligent prediction models such as machine learning, like gradient boosting trees, supervised machine learning and logistic regression which have a great importance in detecting diseases by analyzing medical images and diagnosing chronic diseases, in addition to use deep learning models like deep neural networks, recurrent neural networks, and long short term memory to predict many disease like depression risk, lung cancer, heart diabetic and kidney diseases. Enhance healthcare provider insights using intelligent prediction models to predict future health conditions, treatment outcomes, and disease progression. Moreover, the contribution of the intelligent prediction model helps the healthcare professional identify potential risks and intervene proactively by analyzing patient historical data for early diseases detection like using. Ultimately, the combination of medical history, intelligent prediction, and healthcare data analysis will empower healthcare providers with valuable tools to improve patient outcomes in efficient healthcare organizations.
Keywords
Machine learning; deep learning; health care; prediction; medical history

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