• Srusti M H
• Niharika A
• Siddhi Galada
• Srujana S
• Nagendra R

Genetic disorders present healthcare challenges due to their complex nature. This project aims to develop a predictive model using machine learning to identify genetic disorder risk based on genetic information. It starts with a diverse dataset of genomic data from individuals with known disorders and healthy controls. Preprocessing techniques enhance the dataset, followed by training and evaluating the model with algorithms like SVMs, random forests, and neural networks. Feature importance analysis identifies key markers. The model is fine-tuned with cross-validation for robustness. This research advances predictive medicine, offering a tool for early disorder identification, potentially revolutionizing genetic screening and improving patient outcomes.

"PREDICTING GENETIC DISORDER USING MACHINE LEARNING APPROACHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.e543-e548, May-2024, Available :http://www.jetir.org/papers/JETIR2405464.pdf

"PREDICTING GENETIC DISORDER USING MACHINE LEARNING APPROACHES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppe543-e548, May-2024, Available at : http://www.jetir.org/papers/JETIR2405464.pdf