Application of Machine Learning in Detecting Iron Deficiency Anemia Using Conjunctiva image Dataset from Ghana

Anemia is a global public health issue that mostly emerges as a result of a decrease in red blood cell count and is particularly prevalent in Africa. Invasive ways of detecting anemia are expensive and time-consuming. Anemia may, however, be diagnosed using non-invasive technologies such as machine learning algorithms. In our study, we compared machine learning models to detect anemia using the conjunctiva of the eyes. The main datasets consisting of the conjunctiva of the eyes were acquired using Ghana as a case study for dataset collecting.
Before the study began, the ethical committees at the hospitals involved approved the collection of datasets. Also, because the participants (patients) in the study were minors, the ethical agreement was obtained from their parent(s) or guardian(s), and the purpose and objectives of the study were explained to them, along with the advantages of the health services. Before the participants were enrolled in the data collection, their parent(s) or guardian(s) gave their consent. Furthermore, the ethics and consent committee of the University of Energy and Natural Resources, Ghana approved the start of this experiment. Furthermore, patients' or participants' names and faces were not shown or exposed during image capture, rendering their identification unknown