Personalized medicine is a rapidly evolving field to which many resources have been
devoted recently. It represents a paradigm shift from a one-size-fits-all approach
to healthcare, focusing instead on tailoring treatments and diagnoses to individual patients. This study aims to contribute to this transition by leveraging recent
advancements in microbiome research.
An earlier study that computed the Gut Microbiome Health Index (GMHI), a potent
indicator capable of predicting disease presence ...
Personalized medicine is a rapidly evolving field to which many resources have been
devoted recently. It represents a paradigm shift from a one-size-fits-all approach
to healthcare, focusing instead on tailoring treatments and diagnoses to individual patients. This study aims to contribute to this transition by leveraging recent
advancements in microbiome research.
An earlier study that computed the Gut Microbiome Health Index (GMHI), a potent
indicator capable of predicting disease presence with approximately 73% of accuracy,
serves as the foundation for this research. The GMHI is a numerical value that
classifies a person as healthy if the index is greater than zero, non-healthy if less
than zero, and undetermined if the GMHI is equal to zero. The intent of this study
is to further utilize and develop this index through two primary objectives.
The first objective is to delve deeper into the dataset provided by the original GMHI
study authors, using statistical and Machine Learning (ML) techniques. This process involves a variable analysis, that are species, and the application of classifier
algorithms such as the Support Vector Machine. Upon determining the most informative variables, these will be used as input to a Neural Network (NN) in an
attempt to surpass the existing GMHI prediction accuracy. Through leveraging the
sophisticated pattern recognition capabilities of a NN, the aim is to refine the classification accuracy of health statuses, thereby providing more personalized diagnostic
insights. Two different architectures were used: a Fully Connected NN and an Autoencoder NN. The results obtained from this first objective, though not exceptional,
are promising. The accuracy slightly improved by approximately 3 percent when
using both approaches, indicating better prediction of health conditions.
The second objective is to apply the GMHI to a distinct dataset, which comprises
information on individuals both affected and unaffected by COVID-19. This novel
application of the GMHI represents an innovative investigation into the potential
impact of a viral disease like COVID-19 on an individual’s gut microbiome. Given
the worldwide repercussions of COVID-19, such exploration is crucial. As the potential implications of COVID-19 on the gut microbiome are largely unknown, this
research could provide key insights, influencing patient treatment strategies and
prognosis. Encouraging findings demonstrate that by rescaling and adapting the
GMHI to this specific dataset, the accuracy in detecting a COVID-19 patient significantly improves by 17 percent, thereby achieving an accuracy of 75%, compared
to the accuracy attained when directly using the authors’ formula.
In summary, this study aims to integrate advanced ML techniques to enhance the
GMHI and apply it to a novel dataset. This could significantly contribute to the burgeoning field of personalized medicine, where unique microbiome profiles may inform
diagnostic and therapeutic strategies. The integration of computational techniques
like ML could revolutionize the understanding of disease pathogenesis and treatment
approaches, propelling healthcare into a new era. This project findings may aid in
this direction and could serve as a baseline for further research, as outlined in the
further steps section.
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