Exploring machine learning algorithms in sickle cell disease patient data: A systematic review.

Area of Science:

• Biomedical Informatics
• Computational Biology
• Health Informatics

Background:

• Sickle cell disease (SCD) presents complex diagnostic and monitoring challenges.
• Existing methods for managing SCD require enhancement for improved patient care and outcomes.

Purpose of the Study:

• To systematically review the application of machine learning (ML) algorithms in sickle cell disease (SCD).
• To evaluate ML's role in SCD diagnosis, early organ failure detection, drug dosage identification, and pain intensity classification.

Main Methods:

• Comprehensive literature search and analysis of recent studies applying ML to SCD.
• Inclusion of various ML algorithms such as Multilayer Perceptron, Support Vector Machine, Random Forest, Logistic Regression, LSTM, ELM, CNN, and Transfer Learning.

Main Results:

• ML techniques demonstrate promising results in diagnosing and monitoring SCD.
• Identified ML applications include early detection of organ failure and classification of pain intensity.
• Various ML algorithms show potential for advancing SCD management.

Conclusions:

• Machine learning holds transformative potential for SCD diagnosis, monitoring, and prognosis.
• Challenges include limited dataset sizes, interpretability issues, and overfitting risks.
• Future research should focus on larger datasets, enhanced interpretability, and advanced ML techniques like deep learning.