{'fraction_non_alphanumeric': 0.01834862385321101, 'fraction_numerical': 0.004077471967380225, 'mean_word_length': 5.024539877300613, '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}}
Portable Health Monitoring Module using Machine Learning

Amidst the current covid-19 pandemic situation continuous health monitoring becomes important. In this work, we propose a low cost portable healthcare module which helps in tracking a patient's health conditions using various parameters such as heart rate, carbon dioxide exhalation, body temperature and electrical heart recording (ECG). This monitoring can be done autonomously without the presence of a doctor. This module is helpful in the health monitoring of patients who are in quarantine, or under treatment in a hospital. It can also be used for the health monitoring of elderly and diabled patients. In this work, we also compare some of the existing modules and draw a comparison. In addition to that we also compare different machine learning algorithms used for prediction of asthma. Our results for different algorithms have been quantified and we found that using K neighbors we got the maximum score of 87%.