{'fraction_non_alphanumeric': 0.02103960396039604, 'fraction_numerical': 0.003094059405940594, 'mean_word_length': 5.681818181818182, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detection of Respiratory Emergency Situation of Rescue Patients with Machine Learning Algorithms

Respiratory complications are quite common to the rescue personnel and often require urgent medical intervention to save an emergency patient. During a rescue mission, based on health vitals, patient’s medical history and first responder’s primary impression, a disease is diagnosed to provide first aid. In this paper, we present a detection model developed with machine learning that can help to make a quick diagnosis of respiratory complications and reduce human error in rescue operation. Current methods are mostly focused on detecting selected respiratory diseases from clinically recorded data or long term historical patients’ data. Here we introduce a novel approach of detecting respiratory complications in general instead of focusing on one complication with 9 years of historical data of a rescue station using machine learning based classifiers e.g Support vector machine(SVM), K-Nearest Neighbor(KNN), Gradient Boosting(GB), Extreme Gradient Boosting(XGB) and Random Forest. Additionally, a performance comparison of these algorithms is shown to identify the best detector. Of all the classifiers implementation, the highest detection accuracy was found with support vector machine and boosting algorithms with 91% using 15 attributes; including patient’s real health symptoms, demographic information and primary diagnosis. The outcome of this research presented in the paper can be used by rescue employees worldwide to detect a respiratory situation and save the life of a patient without any delay.