Advancing Immune Disorder Prediction on a Machine Learning Approach for Enhanced Accuracy

Abstract

Immune disorders pose significant challenges to accurate prediction for timely treatment and better patient outcomes. Conventional diagnostic methods often suffer from inaccuracies and inefficiencies. This study introduces a novel approach utilizing machine learning (ML) techniques to enhance forecast accuracy and efficiency. The proposed method involves training ML models using extensive patient datasets comprising genetic markers, medical history, and symptoms. The identification of predictive attributes through feature selection not only enhances model interpretability but also boosts performance. Decision trees, support vector machines, and neural networks are employed on pre-processed data to uncover patterns and relationships crucial for precise predictions. Ensemble learning techniques further refine prediction accuracy. Evaluation metrics demonstrate substantial improvements over existing systems, with the proposed method achieving superior accuracy (0.92), precision (0.91), recall (0.93), and F1-score (0.92). Notably, decision trees (88.7%), support vector machines (86.3%), and neural networks (91.2%) consistently exhibit enhanced model performance. Additionally, the proposed system showcases greater computational efficiency in training (2 hours), prediction (10 ms/instance), and model size (100 MB). The advent of ML-based techniques heralds a transformative shift in immune disease prediction by offering faster, more accurate diagnostics and personalized therapeutic options. By leveraging large-scale patient data and advanced analytics, this approach holds promise for revolutionizing clinical practice and improving patient outcomes in immune disorders.