Recent Studies and Research on Sickle Cell Disease: Statistical Analysis and Machine Learning Approach

Abstract

Machine learning techniques have been successfully applied in various domains of healthcare such as medical imaging, bio-signal processing, pathological data analysis, etc. This chapter discusses the recent studies on sickle cell disease (SCD) based on risk stratification system, predicting the severity of disease, prediction of dosage requirement, prediction of clinical complications of the disease, etc. The blood attributes of SCD patients, which are obtained by high performance liquid chromatography (HPLC) test or complete blood count (CBC) test have been used by many researchers for improving clinical outcomes and therapeutic intervention in SCD. Statistical significance analysis has been reported to determine the correlation and association of pathological attributes with clinical symptoms. Machine learning techniques have been employed for risk stratification and dosage prediction. This chapter summarizes these techniques and suggests research gaps and future challenges.

Key Terms in this Chapter

Statistical Significance: It is the likelihood for the relationship between two or more variable.

Sickle Cell Disease (SCD): It is a type of blood disorder typically inherited from parents.

Artificial Neural Network (ANN): It is a computing system inspired by biological neural network.

Clinical Complications: These are the outcomes of any disease.

Hydroxyurea (HU): It is also known as Hydroxycarbamide used for sickle cell disease patient to increase the level of hemoglobin.

Image Processing: Analysis and manipulation in digital images, in order to improve its appearance, extract and segment features, etc.

Machine Learning (ML): It is a study of algorithms and statistical model to perform specific tasks.