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Recent Studies and Research on Sickle Cell Disease

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.