Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

The COVID-19 virus exhibits pneumonia-like symptoms,including fever,cough,and shortness of breath,and may be fatal.Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities.Clinical studies help to make a correct diagnosis of COVID-19,where the disease has already spread to the organs in most cases.Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread.Therefore,clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold,which are worth investigating.Current supervised Machine Learning(ML)based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography(CT)for disease detection.This strategy relies on a larger clinical dataset and does not focus on early symptom identification.Towards this end,an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms,fever,and cough.Specifically,various metrics,including body temperature,breathing and cough patterns,and physical activity,were considered in this study.Finally,a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 individuals.The proposed technique shows an outlier detection with an accuracy of 89%,on average.

A Machine Learning Approach for Early COVID-19 Symptoms Identification

Symptom identification and early detection are the first steps towards a health condition diagnosis.TheCOVID-19 virus causes pneumonialike symptoms such as fever,cough,and shortness of breath.ManyCOVID-19 contraction tests necessitate extensive clinical protocols in medical settings.Clinical studies help with the accurate analysis of COVID-19,where the virus has already spread to the lungs in most patients.The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography.This is heavily reliant on a larger clinical study and does not emphasize early symptom detection.The aim of this study is to investigate anomalies in patient physiological data for early COVID-19 symptoms identification.In this context,two of the most prevalent symptoms,fever and cough,were examined in a two-fold manner utilizing an unsupervised machine learningmodel.To examine disease progression,physiological features from a chest-worn device were analyzed.First,a Single Vector Activity Index(SVAI)parameter is proposed to monitor the breathing and cough patterns.Second,the dataset’s variance is examined using the DBSCAN method for clustering and outlier detection.Finally,the model accuracy is evaluated to identify outliers on real-time data based on feature dissimilarities,yielding an overall detection accuracy of 90.34%.

Auto-identifying Diagnostic Symptom of Nonlinear Vibration

The technology of diagnostic symptom identification of nonlinear vibration is based on a database of a diagnostic case. This paper defines the periodic degree, quasi periodic degree, and chaotic degree of a Poincare map, an iterated map, and adopts the image identification theory, so the three states of periodic, quasi periodic, and chaotic running states of a machine can be distinguished. It also defines the variable identity, rotating angle and spread degree. The database of diagnostic case is expressed by means of an access database. The diagnostic symptoms are identified using the difference between the Poincare maps of samples and the fault case.Finally, we demonstrate an identification system of a nonlinear vibration diagnostic symptom of large rotating machinery.