A review of Naive Bayes and decision tree methods for predicting particle size distribution in pharmaceutical manufacturing

Abstract

Pharmaceutical manufacturing relies heavily on accurate particle size distribution prediction for drug efficacy, bioavailability, and patient safety. Machine learning algorithms like Naïve Bayes and Decision Tree have gained popularity for their ability to forecast complex data patterns and make informed predictions. However, Naïve Bayes assumes all features are independent, which may compromise the accuracy of predictions in certain scenarios. Researchers have explored hybrid approaches that combine Naïve Bayes with other machine learning algorithms, such as decision trees. The Decision Tree method, which is based on strong data mining methods like multivariate data analysis (MVDA), could help predict important quality factors like particle size distribution. By integrating innovative technologies like nanoelectrodes, the Decision Tree method can enhance efficiency and precision in predicting particle size distribution within pharmaceutical formulations. Accurate particle size distribution prediction is crucial for ensuring the quality and efficacy of pharmaceutical products. Future research should focus on combining Naïve Bayes and Decision Tree methods with advanced machine learning techniques, focusing on feature selection techniques and real-time monitoring and control systems within pharmaceutical manufacturing processes.