Machine Learning-Based Disease Severity Prediction in Sickle Cell Patients: Spectroscopic Insights

Sumit Roy, Saurabh Gupta, Pankaj Jain

2024

Abstract

Sickle cell disease (SCD) presents a significant health challenge with diverse clinical manifestations. Early and accurate prediction of the onset and severity of co-morbidities in SCD is vital for improving outcomes. In this study, we employ advanced healthcare informatics, and machine learning techniques to analyze longitudinal blood pathology data. By focusing on crucial hematological parameters, we gain valuable insights into SCD’s pathophysiology. Additionally, incorporating spectroscopic insights into the study unveils molecular details, enriching the understanding of the disease’s complexity and paving the way for more nuanced and targeted interventions. Utilizing this data, we construct predictive models enabling personalized interventions and advancing precision healthcare management. The research revealed that Random Forest outperforms other algorithms, achieving an accuracy of 88%, recall of 82%, and specificity of 92%. This robust evaluation underscores the model’s reliability in predicting both positive and negative instances. These findings offer a promising pathway for enhancing disease prediction, management, and treatment planning, providing invaluable guidance for clinical practice in the context of sickle cell disease.

Paper Citation

in Harvard Style

Roy S., Gupta S. and Jain P. (2024). Machine Learning-Based Disease Severity Prediction in Sickle Cell Patients: Spectroscopic Insights. In Proceedings of the 12th International Conference on Photonics, Optics and Laser Technology - Volume 1: PHOTOPTICS; ISBN 978-989-758-686-6, SciTePress, pages 51-56. DOI: 10.5220/0012343800003651

in Bibtex Style

@conference{photoptics24,
author={Sumit Roy and Saurabh Gupta and Pankaj Jain},
title={Machine Learning-Based Disease Severity Prediction in Sickle Cell Patients: Spectroscopic Insights},
booktitle={Proceedings of the 12th International Conference on Photonics, Optics and Laser Technology - Volume 1: PHOTOPTICS},
year={2024},
pages={51-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012343800003651},
isbn={978-989-758-686-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Photonics, Optics and Laser Technology - Volume 1: PHOTOPTICS
TI - Machine Learning-Based Disease Severity Prediction in Sickle Cell Patients: Spectroscopic Insights
SN - 978-989-758-686-6
AU - Roy S.
AU - Gupta S.
AU - Jain P.
PY - 2024
SP - 51
EP - 56
DO - 10.5220/0012343800003651
PB - SciTePress