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Data preprocessing plays a critical role in the success of machine learning and deep learning models in the medical and healthcare field. As the availability of healthcare data continues to grow, ensuring its quality, reliability, and suitability for machine learning tasks becomes essential. In this paper, we will try to provide an in-depth exploration of data preprocessing techniques specifically tailored to the medical and healthcare domain. We will cover various steps involved in data preprocessing, including data types, data cleaning, data transforming, and data normalization. Additionally, challenges and considerations unique to medical data preprocessing are discussed.
Chahid et al. (Wed,) studied this question.
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