A Study on the Drug Classification Using Machine Learning Techniques
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This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome
Abstract
This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome
참고문헌 (10)
- Gala, D. V., "Drug classification using machine learning and interpretability", 2021Smart Technologies, Communication and Robotics (STCR), pp.1~8 (2021), IEEE
- Mridha, K., "Explainable Machine Learning for Drug Classification", International Conference on Electrical and Electronics Engineering, pp.673~683 (2023), Springer Nature Singapore
- Chen, C., "Research on Drug Classification Using Machine Learning Model", Highlights in Science, Engineering and Technology, 81, pp.350~355 (2024)
- Gururaj, H. L., "Classification of drugs based on mechanism of action using machine learning techniques", Discover Artificial Intelligence, 1(1), pp.13~ (2021)[DOI]
- Saad, A. I., "Predicting drug interaction with adenosine receptors using machine learning and SMOTE techniques", IEEE Access, 7, pp.146953~146963 (2019)[DOI]
- Shobana, G., "Drug administration route classification using machine learning models", 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp.654~659 (2020), IEEE
- Lee, S., "Explainable Artificial Intelligence for Patient Safety : A Review of Application in Pharmacovigilance", IEEE Access (2023)[DOI]
- Ponzoni, I., "Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery", Wiley Interdisciplinary Reviews:Computational Molecular Science, 13(6), pp.e1681~ (2023)[DOI]
- Puneeth, G. R., "Analysis of drug classification using mechanism of action", J Phys Conf Ser, 1914(1), pp.01204~ (2021)[DOI]
- Zhang, M. L., "ML-KNN : A lazy learning approach to multi-label learning", Pattern recognition, 40(7), pp.2038~2048 (2007)[DOI]