Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

Abstract

One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

“…The paper [19] introduced a model for predicting drug-target interactions (DTIs) using the structural properties of proteins and drugs. In [20], a diverse set of machine learning algorithms was used to assess a combined dataset for predicting COVID-19 based on symptoms. Authors in [21] introduced DeepCOVID-XR, an AI algorithm for detecting COVID-19 on chest radiographs with high accuracy.…”

The Analysis of Support Vector Machine (SVM) on Monthly Covid-19 Case Classification

“…Support Vector Machine (SVM) is one of the approaches of algorithm classification (Support Vector Classification) and regression (Support Vector Regression) which are a supervised machine learning. Support Vector Machine is used to identify hyperplane in a feature quantity or an Ndimension [11], [12], [19], which main purpose is to classify the data dots as clear as possible. In this research the classification process will be using the model of Support Vector Machine Classification.…”

“…There is also other research about prediction of patient covid-19 which uses machine learning algorithm. This research uses five classification method which are Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), and Decision Tree [19]. From this research it is stated that the accuracy of Decision Tree is yet to best, with the highest accuracy at 94,5% followed by KNN, SVM, LR, and GNB.…”