Detection of COVID-19 Using Textual Clinical Data: A Machine Learning Approach

Abstract

Rapid innovation in technology results in fast growth in every field. The field may be a non-medical field, medical field, or any other field of life. In the current century, human society witnessed at least five pandemics. Apart from these five, COVID-19 has also declared a pandemic by the World Health Organization (W.H.O) that has brought a worldwide threat to humankind. The Corona Virus COVID-19 pandemic was originated from Wuhan city of China. Due to the deadly and unpredictable behavior of this COVID-19 virus, until date 2, 00, 26,209 positive cases are reported and the death toll has reached up to 7, 34,025 in 213 countries. To drive in this back-breaking situation, artificial intelligence (AI) is playing an extremely important role. As in the diagnosis of many diseases various AI tools can be used. As of the situation, there is a need for well strong that will help in the detection of the Corona Virus. To make a strong fight against COVID-19, AI can be used in different areas, namely, (a) alarming/alerts (b) tracing and forecasting (c) data console/dashboard (d) diagnosis and prognosis (e) projection and foresight. Machine learning which mainly a sub-part of artificial intelligence (AI) is can also be helpful against the fight with COVID-19. Some machine learning algorithms can be used in the classification of clinical disease reports. Machine learning algorithms may be either classical algorithms or ensemble algorithms. A classification of textual clinical reports can be done thereafter machine learning itself can be applied in the form of feature engineering. Feature engineering can be applied to upgrade the execution of ML algorithms. Term frequency/inverse document frequency (TF/IDF), Bag of Words (BOW) and Report length (RL) are three different mechanisms by which feature engineering can be applied to textual clinical reports. In the case of accuracy measurement, Naive Bayes gains high accuracy than other ML algorithms. A study for comparison of accuracy measurement can also be done for classical and ensemble machine learning algorithms. Besides accuracy, a comparison on the precision, recall, and F1 score can also be done of various machine learning algorithms.