A REAL TIME EMERGENCY DISEASE DIAGNOSIS SYSTEM BASED ON TEXT SAMPLES USING DATA MINING AND DEEP LEARNING ALGORITHMS

Abstract

The increasing demand for rapid and accurate medical diagnosis has highlighted the importance of intelligent healthcare systems capable of supporting emergency disease detection in real time. Traditional diagnostic methods often require significant time, medical expertise, and laboratory analysis, which may delay treatment during critical situations. To address these challenges, this study proposes a real-time emergency disease diagnosis system based on text samples using data mining and deep learning algorithms. The proposed system analyzes patient-provided textual information such as symptoms, medical history, clinical notes, and emergency descriptions to identify possible diseases automatically. Data mining techniques are used for preprocessing, feature extraction, and pattern analysis, while deep learning models such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Natural Language Processing (NLP) algorithms are employed to understand and classify medical text data accurately. The system is capable of predicting diseases in real time and generating diagnostic suggestions to assist healthcare professionals during emergency situations. Experimental results demonstrate improved diagnostic accuracy, faster response time, and efficient handling of large-scale healthcare text data compared to traditional machine learning methods. The proposed approach enhances emergency healthcare services, supports intelligent clinical decisionmaking, reduces diagnostic delays, and contributes to the development of advanced AI-driven healthcare systems.