Text Mining and Machine Learning Framework for Predicting Sickle Cell Disease Research Findings in Nigeria

Abstract

Sickle cell disease (SCD) is a prevalent and complex genetic disorder with a significant impact on public health in Nigeria. The extensive volume of research conducted on SCD underscores the urgent need for effective strategies to analyse and predict research findings to enhance patient care and inform future interventions. This paper reviews an ongoing research seeking to develop a text mining system that leverages advanced computational techniques to predict research outcomes related to SCD in Nigeria. Existing research on SCD in Nigeria has provided valuable insights into various aspects of the disease. However, the sheer magnitude of published research papers, coupled with the unstructured nature of textual data, presents challenges for researchers and healthcare practitioners seeking to gain actionable knowledge from this vast corpus. By harnessing the power of text mining, this research proposes a novel solution to automatically categorise and analyse SCD research findings, enabling efficient retrieval of pertinent information and accelerating the translation of research into practice. The text mining system will employ state-of-the-art natural language processing and machine learning algorithms to extract meaningful information from diverse sources such as biomedical databases and online journals. Through the systematic analysis of these textual data, the system will categorise and predict key findings, including interventions, outcomes, and their relevance to the Nigerian context. Additionally, it will explore patterns, trends, and gaps in the existing research landscape, providing valuable insights for researchers, healthcare practitioners, and policymakers.

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