HELIXCARE: A DNA-based Health Prediction and Drug Recommendation System
DOI:
https://doi.org/10.63503/acset.130Keywords:
Precision Medicine, Pharmacogenomics, Single Nucleotide Polymorphism, Random Forest, Ensemble Learning, Drug-Gene Interaction, PharmGKB, Clinical Decision Support, Health Informatics, SDG 3, SDG 9Abstract
Modern medicine often assumes uniform drug responses, yet genetic variations, especially Single Nucleotide Polymorphisms (SNPs), significantly influence treatment outcomes, leading to adverse reactions and inefficiencies. HelixCare is a web-based precision medicine platform designed to bridge this gap by integrating genomic, clinical and lifestyle data to enable personalised healthcare decisions. The system accepts SNP data (VCF/CSV) along with attributes such as age, BMI, and lifestyle factors, and generates disease risk predictions using an ensemble Random Forest model trained on 106 features. It achieves 87.3% accuracy and an AUC of 0.923, outperforming traditional models including Logistic Regression, SVM, MLP and XGBoost (p < 0.001). HelixCare also incorporates a pharmacogenomic recommendation engine that leverages high-evidence drug–gene interactions from PharmGKB, ClinVar and dbSNP to guide safer medication choices, including dose adjustments and drug avoidance. Built on a React-based frontend and a Flask backend, the system ensures efficient, low-latency performance and secure data handling via AES-256 encryption and role-based access control. By enabling accessible, data-driven insights, HelixCare promotes a shift toward proactive, personalised healthcare aligned with global health innovation goals.
References
[1] H. Wang, Y. Liu, and M. Zhang, "Polygenic risk scores for personalised disease prediction: a systematic review," Nature Reviews Genetics, vol. 24, no. 8, pp. 521 to 539, 2023.
[2] M. Whirl-Carrillo et al., "An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine," Clinical Pharmacology and Therapeutics, vol. 110, no. 3, pp. 563 to 572, 2021.
[3] L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5 to 32, 2001.
[4] T. Chen and C. Guestrin, "XGBoost: a scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785 to 794.
[5] D. R. Crosslin et al., "Genetic variation associated with cardiovascular risk in multi-ethnic cohorts," American Journal of Human Genetics, vol. 105, no. 1, pp. 112 to 125, 2019.
[6] J. P. A. Ioannidis, "Why most discovered true associations are inflated," Epidemiology, vol. 19, no. 5, pp. 640 to 648, 2008.
[7] S. M. Lundberg and S. I. Lee, "A unified approach to interpreting model predictions," in Advances in Neural Information Processing Systems, vol. 30, 2017, pp. 4765 to 4774.
[8] M. J. Landrum et al., "ClinVar: improving access to variant interpretations and supporting evidence," Nucleic Acids Research, vol. 46, no. D1, pp. D1062 to D1067, 2018.
[9] S. T. Sherry et al., "dbSNP: the NCBI database of genetic variation," Nucleic Acids Research, vol. 29, no. 1, pp. 308 to 311, 2001.
[10] D. Roden et al., "Pharmacogenomics: the genetics of variable drug responses," Circulation, vol. 123, no. 15, pp. 1661 to 1670, 2011.
[11] M. Pirmohamed, "Pharmacogenomics: current status and future perspectives," Nature Reviews Genetics, vol. 24, no. 6, pp. 350 to 362, 2023.
[12] World Health Organization, "Genomics and world health: report of the advisory committee on health research," WHO Press, Geneva, 2022.
[13] P. Danecek et al., "The variant call format and VCFtools," Bioinformatics, vol. 27, no. 15, pp. 2156 to 2158, 2011.
[14] F. Pedregosa et al., "Scikit-learn: machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825 to 2830, 2011.
[15] A. Grinberg, Flask Web Development: Developing Web Applications with Python, 2nd ed. Sebastopol, CA: O'Reilly Media, 2018.
[16] React Documentation Team, "React 18 official documentation," Meta Open Source, 2023. [Online]. Available: https://react.dev
[17] United Nations, "Sustainable development goals: goal 3 good health and well-being," UN Department of Economic and Social Affairs, 2015. [Online]. Available: https://sdgs.un.org/goals/goal3
[18] United Nations, "Sustainable development goals: goal 9 industry, innovation and infrastructure," UN Department of Economic and Social Affairs, 2015. [Online]. Available: https://sdgs.un.org/goals/goal9
[19] Office for Civil Rights, "HIPAA security rule technical safeguards," U.S. Department of Health and Human Services, 2013.
[20] European Parliament, "General Data Protection Regulation (GDPR), Regulation (EU) 2016/679," Official Journal of the European Union, 2016.
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