COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

Fetal health classification using supervised learning approach

Fetal Health monitoring is important to reduce or minimize the mortality of both mother and child. This paper presents a study on a dataset of 2126 records on features extracted from cardiotocography exam with 21 attributes including baseline value accelerations, fetal movement, uterine contractions, light, severe and prolonged decelerations, abnormal short-term variability, the mean value of short-term variability, percentage of time with abnormal long-term variability, the mean value of long-term variability, histogram width, min, max, number of peaks, number of zeroes, mode, mean, median, variance, and tendency. This paper will be using Supervised Machine Learning to compare and classify the data set using K-NN, Linear SVM, Naive Bayes, Decision Tree (J4S), Ada Boost, Bagging and Stacking. Lastly, Bayesian networks are then developed and compared with the other classifier. By comparing all of the classifiers, classifier Ada Boost with sub-model Random Forest has the highest accuracy 94.7% with k = 10

Covid-19 severity classification using supervised learning approach

This paper presented work on supervised machine learning techniques using K-NN, Linear SVM, Naïve Bayes, Decision Tree (J48), Ada Boost, Bagging and Stacking for the purpose to classify the severity group of covid-19 symptoms. The data was obtained from Kaggle dataset, which was obtained through a survey collected from the participant with varying gender and age that had visited 10 or more countries including China, France, Germany Iran, Italy, Republic of Korean, Spain, UAE, other European Countries (Other-EUR) and Others. The survey is Covid-19 symptom based on guidelines given by the World Health Organization (WHO) and the Ministry of Health and Family Welfare, India which then classified into 4 different levels of severity, Mild, Moderate, Severe, and None. The results from the seven classifiers used in this study showed very low classification results