Artificial Neural Network as a Tool for Appraising Hematological Parameters in Sudanese Patients with Malaria.

Background: The purpose of this paper was to quantitatively assess and explore the effect of malaria infection in the hematological parameters of Sudanese population.

Methods: All data were obtained from malaria infected and non-infected Sudanese patients attending to Kosti Teaching Hospital. The effect of malaria on the hematological parameters was depicted. The relationships between hematological parameters with the effect of malaria in the two groups were assessed using Spearman's correlation. The quantitative effects of malaria on the hematological parameters were assessed using SPSS 21.0 software with a neutral network feature.

Results: There was no correlation between the effect of malaria infection and MCV, PCV, and MCHC (r = 0.055, r = 0.087, and r = 0.067, respectively) among Sudanese population (p > 0.05). An obvious correlation was observed between hemoglobin concentration (Hb Conc.), RBC count, MCH, and ESR and the effect of malaria infection (r = -0.226, r = 0.285, r = 0.286, and r = -0.378, respectively (p < 0.05). The effect of malaria infection on PCV standardized percentage (100%) was much higher than other hematological parameters.

Conclusions: Changes in the PCV measurements among Sudanese patients may be associated with the higher effect of malaria infection. Because malaria is caused by a blood parasite, early diagnosis and treatment could contribute to improve the individual health status.