Title of article :
Designing a Human T-Lymphotropic Virus Type 1 (HTLV-I) Diagnostic Model Using the Complete Blood Count
Author/Authors :
Sarbaz, Masoumeh Department of Medical Informatics - School of Medicine and Faculty Member of Health Information Technology and Medical Records Department - School of Paramedical Sciences - Mashhad University of Medical Sciences , Pournik, Omid 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 , Ghalichi, Leila Department of Epidemiology and Biostatistics - School of Public Health - Tehran University of Medical Sciences , Kimiafar, Khalil 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 , Razavi, Amir Reza Department of Medical informatics - Faculty of Medicine - Mashhad University of Medical Sciences
Abstract :
Objective(s): 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. Materials and 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. Conclusion: 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.
Keywords :
CHAID , Machine Learning , Decision Tree , HTLV-I
Journal title :
Astroparticle Physics