  Sickle cell anemia is a genetic disorder that is widespread in many regions
of the world. Early diagnosis through screening and preventive treatments are
known to reduce mortality in the case of sickle cell disease (SCD). In
addition, the screening of individuals with the largely asymptomatic condition
of sickle cell trait (SCT) is necessary to curtail the genetic propagation of
the disease. However, the cost and complexity of conventional diagnostic
methods limit the feasibility of early diagnosis of SCD and SCT in
resource-limited areas worldwide. Recently, our group developed a low-cost
UV-Vis absorbance spectroscopy based diagnostic test for SCD and SCT. Here, we
propose an AutoML based approach to classify the raw spectra data obtained from
the developed UV-Vis spectroscopy technique with high accuracy. The proposed
approach can detect the presence of sickle hemoglobin with 100% sensitivity and
93.84% specificity. This study demonstrates the potential utility of the
machine learning-based absorbance spectroscopy test for deployment in mass
screening programs in resource-limited settings.
