Personalized Treatment Planning using Machine Learning Models: A Review

Abstract

Data preprocessing is a critical determinant of machine learning (ML) model performance, yet systematic synthesis of its techniques and outcomes remains limited. This review adheres to the PRISMA guidelines for evaluating normalization, standardization, missing value imputation, categorical encoding, and data augmentation. The research screened 1,250 studies from IEEE Xplore, PubMed, and Scopus (2015–2023), selecting 29 peer-reviewed articles meeting inclusion criteria (empirical validation, comparative analysis, and reproducibility). The findings reveal that normalization and standardization improve the accuracy of the neural network by up to 15%, while advanced imputation (e.g., MICE) increases performance by 10 to 20% over mean / median imputation. One shot encoding suits low cardinality categorical data, whereas target encoding outperforms for high-cardinality features. Data augmentation enhances image classification ac- curacy by 25%. Key challenges include computational costs, overfitting, and data quality biases. Iterative preprocessing and cross-validation emerge as best practices. The review highlights gaps in scalable preprocessing for high-dimensional data and calls for standardized benchmarks.

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