Digital Technologies Research and Applications

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On the Use of Raman Blood Spectroscopy and Prediction Machines for Enhanced Care of Endometriosis Patients

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https://doi.org/10.54963/dtra.v2i2.94
Nsugbe, E. (2023). On the Use of Raman Blood Spectroscopy and Prediction Machines for Enhanced Care of Endometriosis Patients. Digital Technologies Research and Applications, 2(2), 1–12. https://doi.org/10.54963/dtra.v2i2.94
Volume 2 Issue 2: September 2023
Copyright (c) 2023 Ejay Nsugbe
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Authors

  • Ejay Nsugbe
    Nsugbe Research Labs

Endometriosis is a prevalent disease of the female endometrium which affects women of all ethnicities and has been seen to be most common in the 25–35 years age group. The disease does not have a definitive cure, hence care and management are the essential components towards dealing with the disease. At present, the predominant means towards the diagnosis of the presence of the disease involves different imaging modalities alongside laparoscopy, where the instrumentation is expensive to acquire and requires clinical expertise. Recently, work has been done by an author who leveraged Raman blood spectroscopy alongside machine learning towards an affordable high throughput means towards the prediction of endometriosis.

This work utilises the Raman blood spectroscopy dataset alongside advanced signal processing, machine learning and clinical cybernetics, towards the design of a prediction machine which sits within a clinical framework to facilitate Human-Machine interaction for an enhanced care strategy for patients with endometriosis. The prediction machine is designed to initially predict whether a patient has the disease, and is then followed by the use of unsupervised learning to form an inference means towards predicting the extent of the disease. The results showed that a combination of the adopted methods could allow for a high prediction of the endometriosis disease. Subsequent work in this area would now include further optimisation of the prediction machine in order to potentially maximise the prediction accuracy.

Keywords:

Machine Learning Public Health Endometriosis Obstetrics and Artificial Intelligence

Author Biography

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