Abstract—Tuberculosis (TB) remains one of the leading
health problems in the Philippines. Although curable, many
Filipinos cannot afford the cost of treatment. Furthermore, the
free services offered by the public health centers are insufficient
to attend to the medical needs of those seeking help. In this
paper, the researchers present the system that can assist
doctors, nurses and health workers in diagnosing tuberculosis
using techniques in machine learning through applying ID3
algorithm. Interviews with several experts in the field of TB
were conducted in order to gather data that were used to
populate the system’s knowledge base. Test result show that the
system is capable of prescribing treatments based on the
patient’s data and tracking the progress of the patient based on
his/her prescribed treatment.
Index Terms—Diagnostic, machine learning, tuberculosis,
treatment.
Arman James E. Avenir is with Aegies People Support Center, Ayala Ave
Cor. Gil Puyat Ave Makati City, Philippines (e-mail:
armanavenir@gmail.com).
Jonas D. Villota is with Blinkup Inc. 1404 Prime Land Tower, 2218
Market Street Madrigal Business Park Ayala Alabang, Muntinlupa City,
Philippines (e-mail: jonsader.14@yahoo.com).
Maryli F. Rosas is with the Computer Studies Department of De La Salle
University – Dasmarinas, City of Dasmarinas Cavite, Philippines 4115
(e-mail: ilyramrosas@yahoo.com).
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Cite:Arman James E. Avenir, Jonas D. Villota, and Maryli F. Rosas, "Machine Learning: A Means of Diagnosing and Prescribing Treatments for Tuberculosis Patients," International Journal of Computer Theory and Engineering vol. 7, no. 3, pp. 201-206, 2015.
