PREDICTION OF BRONCHITIS DISEASE INDICATIONS USING THE CATBOOST ALGORITHM
Keywords:
Machine Learning, CatBoost, Bronchitis Disease Prediction, Classification.Abstract
Bronchitis is one of the respiratory diseases classified as Acute Respiratory Infection (ARI), characterized by prolonged cough, shortness of breath, and fever. Accurate prediction of bronchitis indications can assist in early diagnosis and improve the efficiency of healthcare services. This study applies the CatBoost algorithm to predict bronchitis indications based on patient symptom data obtained from an apothecary dataset. The research stages include data collection, data cleaning, labeling, feature engineering, data splitting, hyperparameter tuning using GridSearchCV, model training, and model evaluation. The evaluation was carried out using Accuracy, Precision, Recall, and F1-Score metrics. The results show that the 60:40 data split scenario produced the best performance with an accuracy of 81.81%, precision of 75.23%, recall of 79.66%, and an F1-Score of 77.43%. These findings indicate that the CatBoost algorithm can classify bronchitis indications with good and stable performance
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