  Despite the observable benefit of Natural Language Processing (NLP) in
processing a large amount of textual medical data within a limited time for
information retrieval, a handful of research efforts have been devoted to
uncovering novel data-cleaning methods. Data cleaning in NLP is at the centre
point for extracting validated information. Another observed limitation in the
NLP domain is having limited medical corpora that provide answers to a given
medical question. Realising the limitations and challenges from two
perspectives, this research aims to clean a medical dataset using ensemble
techniques and to develop a corpus. The corpora expect that it will answer the
question based on the semantic relationship of corpus sequences. However, the
data cleaning method in this research suggests that the ensemble technique
provides the highest accuracy (94%) compared to the single process, which
includes vectorisation, exploratory data analysis, and feeding the vectorised
data. The second aim of having an adequate corpus was realised by extracting
answers from the dataset. This research is significant in machine learning,
specifically data cleaning and the medical sector, but it also underscores the
importance of NLP in the medical field, where accurate and timely information
extraction can be a matter of life and death. It establishes text data
processing using NLP as a powerful tool for extracting valuable information
like image data.
