Enhancing Diagnostic Accuracy with AI-Powered Symptom Checkers: A Comparative Analysis of Natural Language Processing and Decision Tree Algorithms

Abstract

This study explores the efficacy of AI-powered symptom checkers in improving diagnostic accuracy by comparing two prominent algorithms: Natural Language Processing (NLP) and Decision Trees. Driven by the increasing demand for efficient and reliable preliminary medical assessments, this research aims to delineate the respective advantages and limitations of these algorithms in symptom analysis. The methodology involves the deployment of both NLP and Decision Tree models on a large dataset comprising various symptoms and corresponding diagnoses. Performance metrics such as precision, recall, and F1 score are utilized to evaluate diagnostic accuracy and computational efficiency. The results demonstrate that NLP models, with their advanced capability to understand and interpret intricate linguistic patterns, outperform Decision Trees in scenarios involving complex symptom descriptions. Conversely, Decision Trees exhibit superior speed and transparency in simpler diagnostic cases, providing clear decision paths and easily replicable results. Furthermore, integration challenges, including data standardization and model interpretability, are analyzed to provide a comprehensive overview of deploying AI in medical diagnostics. This paper ultimately highlights that while both algorithms have distinct roles, a hybrid approach leveraging the strengths of NLP's nuanced understanding and the logical clarity of Decision Trees could potentiate enhanced diagnostic frameworks, paving the way for more robust and reliable AI-assisted healthcare solutions.