STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Genetic Modifications and Recombinations Review Article

FutureGen – Predicting Genetic Health

  1. Omkar Sanjay Kamble MCA Department, P. E. S. Modern College of Engineering
  2. Soham anand borage MCA Department, P. E. S. Modern College of Engineering
  3. Tanay Prabhakar Pandit MCA Department, P. E. S. Modern College of Engineering
  4. Shaikh Mohammed Zaid Manzoor MCA Department, P. E. S. Modern College of Engineering
  5. Bhavesh santosh katkar MCA Department, P. E. S. Modern College of Engineering
  6. Rama Bansode MCA Department, P. E. S. Modern College of Engineering

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

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