Implementation of the Naïve Bayes Algorithm to Predict the Severity Status of Tuberculosis Patients in Dr. M. Djamil Central General Hospital

Nurul Abdillah, Alfita Dewi, Herman Susilo, Dede Fauzi

Abstract


Tuberculosis (TB) remains a serious public health issue in Indonesia, including in Padang City. Accurate and timely intervention is crucial, especially in predicting the severity status of TB patients to enable faster and more appropriate medical responses. Currently, hospital systems still face limitations in classifying the severity status of TB patients, which may result in delayed treatment. This study aims to apply the Naïve Bayes Classifier (NBC) algorithm to predict the severity status of TB patients in Padang City. The medical record data used were obtained from Dr. M. Djamil Central General Hospital and Dr. Rasidin Regional General Hospital, consisting of 227 TB patient records from April to June 2024. The dataset includes 13 attributes, such as gender, age, type of cough, shortness of breath, chest pain, and severity status as the class attribute. The results show that the NBC algorithm achieved an accuracy of 71.81% in predicting patient severity status. This study is expected to support healthcare professionals in making more informed decisions for patient management planning.

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References


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Publish by Universitas Syedza Saintika Padang(Jl. Prof. Dr. Hamka No. 228 Air Tawar Timur Padang)

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