FutureGen – Predicting Genetic Health

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Omkar Sanjay Kamble,

  • Soham anand borage,

  • Tanay Prabhakar Pandit,

  • Shaikh Mohammed Zaid Manzoor,

  • Bhavesh santosh katkar,

  • Rama Bansode,

  1. Student, MCA Department, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  2. Student, MCA Department, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  3. Student, MCA Department, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  4. Student, MCA Department, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  5. Student, MCA Department, P. E. S. Modern College of Engineering, Pune, Maharashtra, India
  6. Assistant Professor, MCA Department, P. E. S. Modern College of Engineering, Pune, Maharashtra, India

Abstract

FutureGen is an intelligent web-based system developed to help couples assess the risk of genetic disorders in their future child through data-driven analysis. The system brings together modern web technologies and machine learning to offer accurate and accessible predictions. The frontend, built with React, provides an intuitive interface for user interaction, while a Flask-based backend API handles model inference and manages communication with the Supabase database, which securely stores user and prediction data. The machine learning engine, powered by a Random Forest classifier, processes clinical and parental information such as age, carrier status, and family medical history to estimate the likelihood of hereditary disorders. By integrating AI with healthcare insights, FutureGen aims to promote early awareness, support genetic counseling, and assist couples in making informed family planning decisions.

Keywords: Genetic Disorder Prediction, Machine Learning, Parental Health Data, AI in Healthcare, Genetic Risk Assessment

How to cite this article:
Omkar Sanjay Kamble, Soham anand borage, Tanay Prabhakar Pandit, Shaikh Mohammed Zaid Manzoor, Bhavesh santosh katkar, Rama Bansode. FutureGen – Predicting Genetic Health. International Journal of Genetic Modifications and Recombinations. 2026; 04(01):-.
How to cite this URL:
Omkar Sanjay Kamble, Soham anand borage, Tanay Prabhakar Pandit, Shaikh Mohammed Zaid Manzoor, Bhavesh santosh katkar, Rama Bansode. FutureGen – Predicting Genetic Health. International Journal of Genetic Modifications and Recombinations. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijgmr/article=2026/view=239624


References

[1] Raza, A., et al. (2022). Predicting Genetic Disorder and Types of Disorder Using Machine Learning Techniques. International Journal of Intelligent Systems.

[2] Yalçın, E., et al. (2025). Artificial Intelligence in Prenatal Diagnosis: Down Syndrome Risk Assessment Using Machine Learning Models. PubMed Central (PMC).

[3] Bracher-Smith, M., et al. (2021). Machine Learning for Genetic Prediction of Psychiatric Disorders: A Systematic Review. Nature Reviews Genetics.

[4] Raben, T., et al. (2021). Integrating Genomic Data and Clinical Parameters for Disease Risk Prediction Using AI Models. Springer Nature.

[5] Yousaf, M. B., et al. (2024). Hybrid Machine Learning Model for Predicting Genetic Disorders Using Clinical and Family History Data. Elsevier ScienceDirect.

[6] Bahado-Singh, R. O., et al. (2019). Machine Learning and Artificial Intelligence in Prenatal Genomics: Applications in Fetal Health Prediction. American Journal of Obstetrics and Gynecology.

[7] Naik, S., et al. (2022). Implementation of Machine Learning Algorithms in Predictive Healthcare Systems. AIMS Press.

[8] Shetty, H. P., Patil, M. S., & Yadav, S. (2024). Predictive Modeling for Genetic Disorder Identification Using Ensemble Learning Techniques. SpringerLink.

[9] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

[10] Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29.

Ahead of Print Subscription Review Article
Volume 04
01
Received 21/02/2026
Accepted 31/03/2026
Published 03/04/2026
Publication Time 41 Days
87

Login


My IP

PlumX Metrics