Designing a Human T-Lymphotropic Virus Type 1 (HTLV-I) Diagnostic Model using the Complete Blood Count.
OBJECTIVE
Infection caused by Human T-Lymphotropic Virus Type 1 (HTLV-I) can be observed in some areas of Iran in form of endemic. Most of the cases are asymptomatic, and few cases progress to malignancies and neural diseases. Designing and implementing a model to screen people especially in endemic regions can help timely detection of infected people and improve the prognosis of the disease.


METHODS
In this study, results of the complete blood count (CBC-diff) for 599 healthy people and the patients with different types of Leukemia and HTLV-I have been examined. Modeling was made using CHAID method. The final model was carried out based on the number of white blood cells (WBC), platelets, and percentages of eosinophils.


RESULTS
The accuracy of the final model was 91%. By applying this model to the CBC-diff results of people without symptoms or miscellaneous patients in endemic regions of our country, disease carriers can be identified and referred for supplementary tests.


CONCLUSIONS
With regard to the prevalence of different complications in infected people, these individuals can be identified earlier, leading to the improvement of the prognosis of this disease and the increase of the health status especially in endemic regions.
Department of Medical Informatics, School of Medicine and Faculty Member of Health Information Technology and Medical Records Department, School of Paramedical Sciences, Mashhad Univer-sity of Medical Sciences, Mashhad, Iran.
Masoumeh
M
Sarbaz
Department of Medical informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran and Deputy for Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Omid
O
Pournik
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Leila
L
Ghalichi
Department of Health Information Technology and Medical Records, School of Paramedical Sciences, Mashhad University of Medical Sciences, Mashhad, Iran and Department of Health Information Management, Faculty of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, I.R. Iran.
Khalil
K
Kimiafar
Department of Medical informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Amir Reza
AR
Razavi
CHAID; Decision Tree; HTLV-I; Machine Learning