Artificial Intelligence based prediction system for General Medicine

COVID-19 is highly infectious and has been extensively spread worldwide, with approximately 651 million definite cases crosswise the globe including Pakistan. At that era of pandemic where patients are not able to approach a doctor for even the routine checkups, in such curial situation even normal disease checkups are ignored by many families due to pandemic situations, those diseases may lead to be a perilous disease are results of it. Human disorders portray scenarios that even disturb or permanently cutoff the essential functions of a body parts. Consequently, the aim is to transform raw health data potential into actionable insights to applying the promising outcomes of Body Sensor Network (BSN) and State-of-Art Artificial Intelligence (AI) techniques to get proper medicine allocation to the particular health state of patient. In this paper the different techniques of Deep Learning and Machine Learning introduced to predict the actual medicine for the specific health state of patient according to data from the BSN. Experiments have been conducted on large dataset which shepherd it into 16 states of patient's health which will allotted to AI model to predict the medicine accordingly to the health state of patient. Experimental results show the 87.46% by Random Forest, 92.74% by K-Nearest Neighbors, 74.57% by Naive Bayes, 94.41% by Extreme Gradient Boost, 84.88% by Multi-Layer Perceptron in terms of precision of model training in event of classification. © 2023 IEEE.

A Novel Deep Learning based Black Fungus Detection using the Bagging Ensemble with K-Nearest Neighbor

Machine learning is widely employed, and broadly speaking, scientists consider applying it everywhere. Around the same period, we can see that India has been devastated by the second corona wave. In a single day, more than 4 lakh instances arrive. Meanwhile, reports of the arrival of a new, fatal fungus called Mucormycosis emerged (Black fungus). Additionally, this fungus expanded quickly throughout numerous states, leading some of them to designate this illness an epidemic. People with weak immunity functions, including those who have had the corona virus and some of whom are still recovering, are more likely to get a black fungus infection since their bodies can't successfully fight it off. Bagging Ensemble with K-Nearest Neighbor is a modified machine learning approach that will be developed in this study (BKNN). The traditional methods, including K-Nearest Neighbor ensemble with bagging classification, are the basis for the suggested methodology. After the image processing techniques, including pre-processing and segmentation, were reviewed, the accuracy score for this classifier was 96.4 percent, which would have been the highest of all the findings. This paper described how machine learning was beneficial during the Corona era, much as it would be beneficial during epidemics like mucormycosis. The last section of this essay presents accurate, graphical evidence for all items addressed, along with explicit specifications. © 2022 IEEE.

Face Completion with Multi-Output Estimators

Since the beginning of the COVID-19 pandemic, images of faces with obscured bottom halves have become more common due to masking. Now more than ever, end-users are looking toward machine learning and data science to create high-quality replacements for missing facial data. For face completion, we evaluate multiple machine learning algorithms, including Decision Trees, K-Nearest Neighbors, and Support Vector Machines. Since most of the existing work in this field uses deep learning, we explore the impact of using multiple deep learning techniques and use them as a point of comparison. Our study indicates that despite the conventional norm that deep learning algorithms outperform their machine learning counterparts, the non-deep learning techniques perform better for this application.11Code is available at https://github.com/nickfons/fcwmoe. © 2022 IEEE.

Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

In this analysis, the methods used are the K-Nearest Neighbor classification method and the Logistic Regression classification method with data taken on the twitter application. This study examines the level of accuracy in public sentiment regarding covid-19 vaccination with positive and negative labels. The AUC value in the KNN algorithm with TextBlob labeling is 0.765 with and 0.76S for VaderSentiment labeling are both included in the fair classification criteria. Meanwhile, the Logistic Regression algorithm produces an accuracy of 84.97% with a ratio of 90:10 for Labeling TextBlob, while for Labeling VaderSentiment with a ratio of 90:10 results in an accuracy of 85.22%. Both algorithms are validated using K-Fold Cross Validation with a fold count of 10. The comparison results obtained when conducting an evaluation with the confusion matrix showed that the Logistic Regression algorithm with VaderSentiment labeling had the highest accuracy value compared to the K-Nearest Neighbor algorithm with TextBlob and VaderSentiment labeling. © 2022 IEEE.

kNN-SVM with Deep Features for COVID-19 Pneumonia Detection from Chest X-ray

Most attention has been paid to chest Computed Tomography (CT) in this burgeoning crisis because many cases of COVID-19 demonstrate respiratory illness clinically resembling viral pneumonia which persists in prominent visual signatures on high-resolution CT befitting of viruses that damage lungs. However, CT is very expensive, time-consuming, and inaccessible in remote hospitals. As an important complement, this research proposes a novel kNN-regularized Support Vector Machine (kNN-SVM) algorithm for identifying COVID-induced pneumonia from inexpensive and simple frontal chest X-ray (CXR). To compute the deep features, we used transfer learning on the standard VGG16 model. Then the autoencoder algorithm is used for dimensionality reduction. Finally, a novel kNN-regularized Support Vector Machine algorithm is developed and implemented which can successfully classify the three classes: Normal, Pneumonia, and COVID-19 on a benchmark chest X-ray dataset. kNN-SVM combines the properties of two well-known formalisms: k-Nearest Neighbors (kNN) and Support Vector Machines (SVMs). Our approach extends the total-margin SVM, which considers the distance of all points from the margin;each point is weighted based on its k nearest neighbors. The intuition is that examples that are mostly surrounded by similar neighbors, i.e., of their own class, are given more priority to minimize the influence of drastic outliers and improve generalization and robustness. Thus, our approach combines the local sensitivity of kNN with the global stability of the total-margin SVM. Extensive experimental results demonstrate that the proposed kNN-SVM can detect COVID-19-induced pneumonia from chest X-ray with greater or comparable accuracy relative to human radiologists. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Human Body Temperature and Face Mask Audit System for COVID Protocol

The world has suffered enough in the aspect of COVID-19. From the year 2019, all we have in our hearts is a constant fear and terror of becoming prey to this deadly virus that has almost taken five lakh and twenty-five thousand lives to date within India, as the statistics show. The way to ensure that you maintain proper public hygiene is by ensuring that you wear masks in public places. There have been many algorithms that provide quicker results. We have tested our model in K-Nearest Neighbors (KNN), Support Vector Machine (SVM) algorithms and using deep learning technique Convolution Neural Networks (CNN). Comparing others, CNN provides more accuracy and has a shorter latency. Thus, we have implemented human face mask detector using CNN. The body temperature of the individual entering a room is monitored by the support of myDAQ, NI Instruments. If the body temperature is higher than 99F, then the person entering the space is not permitted inside. We have designed a device that monitors the temperature of the person entering the room along with the monitoring of face masks using the webcam. © 2022 IEEE.

Ensemble Learning for Sentiment Analysis on Twitter Data Related to Covid-19 Preventions

The World Health Organization (WHO) declared the 2019 Coronavirus disease outbreak (Covid-19) as a pandemic and made it a trending topic on social media platforms, such as Facebook and Twitter. Unfortunately, news and opinions shared on social media affect people's mentality and create panic situations in society, but in the other hand, these opinions can be analyzed using sentiment analysis approach to generate knowledge and insight for the local government to monitor people reaction to the policies that have been issued to prevent the outbreak of Covid-19 virus. Therefore, this work aimed to propose an ensemble learning model that can classify the sentiment inside the people's opinions from Twitter. The ensemble model used Naïve Bayes Classifier, C4.5, and k-Nearest Neighbors as base learners with voting mechanism to generate the final decision. For learning, the ensemble model used a dataset containing 3884 clean data that was successfully downloaded using Twitter API related to Covid-19 outbreak prevention and processed using TF-IDF method. The dataset has two classes, i.e., 'positive' and 'negative' to represent the sentiment of the opinion in each data. The proposed model got 80.61% of accuracy, 79.49% of recall, and 81.20% of precision, after being evaluated using 10-fold Cross Validation. It also performed better when compared to several learning models using only single Machine Learning algorithm. © 2022 IEEE.

Analysis of Tuberculosis Disease Spreading Pattern in Muara Enim District using KNN Algorithm

Tuberculosis (TB) is a type of infectious disease caused by Mycobacterium tuberculosis, which not only attacks the lungs, but can also attack the bones, intestines, or glands. During the Covid-19 pandemic, TB cases in Indonesia also increased. TB and Covid-19 had the similar symptoms such as cough, fever, and breathing difficulty, so that TB sufferers must be given serious treatment to avoid Covid-19. In predicting a disease, it is important for health workers to make decisions, thus it is necessary to do an early diagnosis in order to reduce the transmission of TB in the community. There are many algorithm methods used in conducting data analysis, for this study the authors use K-Nearest Neighbor (K-NN) algorithm and Logistic Regression as comparison. Experimental results using available dataset collected from health centers in Muara Enim District of South Sumatra Province show that the K-NN algorithm provides the best accuracy of 89% on dataset with training to testing data ratio of 80%:20%, while the Logistic Regression provides the best accuracy of 96% on 70%:30% ratio. The analysis mechanism discussed in this paper may be considered as tool for the authority to predict and take necessary actions to prevent the TB spreading. © 2022 ACM.

Comparison of Naïve Bayes, C4.5 and K-Nearest Neighbor for Covid-19 Data Classification

Covid-19 is a new virus that appeared in the city of Wuhan in 2019. This virus spreads very quickly even to Indonesia. One effort that can be done to detect the presence of this virus is the PCR and antigen test. Increasing this case resulted in a medical team having difficulty detecting suspects exposed to viruses. This research was conducted to find the best classification algorithm in predicting and classifying status on the suspected Covid-19 both exposed or not exposed. The method used in this study is Naïve Bayes, C4.5 and K-Nearest Neighbor which have very high accuracy using secondary data from the Dumai City Health Agency. From this study it was found that the algorithm C4.5 as the best algorithm in predicting the status of COVID-19 patients, especially in the city of Dumai with an accuracy of 86.54%, recall 71.51%and precision 85.14%. This study has implications for further researchers in choosing an algorithm to predict the COVID-19 case. © 2022 IEEE.

Detection of COVID-19 Using Classification of an X-Ray Image Using a Local Binary Pattern and K-Nearest Neighbors

The recently identified coronavirus pneumonia, which was later given the name COVID-19, is a virus that can be fatal and has affected more than 300,000 individuals around the world. Because there is currently no antiviral therapy or vaccine that has been granted approval by the FDA to cure or prevent this sickness, an automatic method for disease identification is required because of the fast global distribution of this exceedingly contagious and lethal virus. A unique machine learning strategy for automatically detecting this ailment was discovered. Machine learning approaches should be applied in essential jobs in infectious illnesses. As a result, our major aim is to use computer vision algorithms to identify COVID-19 without the need for human interaction. This paper suggested using image processing to classify objects and make early detections using X-ray pictures. Features are extracted for this region using a variety of techniques, including (LBP), (HOG), and use K-Nearest Neighbor algorithm (KNN) for classification, with training percentages of 50%, 60%, 70%, 80%, and 90%. Experiments indicated that using the suggested approach to identify X-ray photos of corona patients, it is feasible to diagnose the disease using X-ray images by training the device on the image data set (about 2,400 photos). The results were tested on the average of the samples taken (random 2000 images) each time and the measurement of multiple training ratios (50%, 60%, 70%, 80%, and 90%). The experimental findings revealed remarkable prediction accuracy in all investigated scenarios, ranging from 85% to 99%. © 2022 IEEE.

Implementation of K-Nearest Neighbor to Predict the Chances of COVID-19 Patients' Recovery

Coronavirus Disease 2019 (COVID-19) is a new disease discovered in 2019 in Wuhan, China, and then spread worldwide. Many victims have confirmed varying positive levels of infection based on the patient's immunity. This study aimed to predict the chances of COVID-19 patients' recovery based on the patient's symptoms and conditions. The method used is the K-Nearest Neighbor (KNN) algorithm. KNN produces two classes of predictions: the chance of recovering or the possibility of dying. Based on the experimental results on 496 data from patients who were confirmed positive for COVID-19, KNN predicted the chances of recovery for patients with confirmed COVID-19 with an average accuracy of 88.16%. A prediction system for the chance of recovery for COVID-19 patients is constructed by choosing the best model from five test scenarios based on the given k value. The best model is at a value of k equal to 4, with an accuracy value of 88.8%. © 2022 IEEE.

COVID-19 X-Ray Images Classification using Support Vector Machine and K-Nearest Neighbor

COVID-19 has significantly influenced living in recent years. Almost all countries have carried out all limitations to reduce its spread. Detection is highly required for further handling of COVID-19. In this study, the detection was performed using classification on 1,184 X-ray images, specifically 404 X-ray images of COVID-19 positive people, 390 X-ray images of normal people and 390 X-ray images of pneumonia positive people. The image data were extracted with the Haar wavelet algorithm and classified using the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN);each had three classification models. The Quadratic SVM model obtained the best result with an accuracy of 79.8%. © 2022 IEEE.

A Comparison of Machine Learning Methods for best Accuracy COVID-19 Diagnosis Using Chest X-Ray Images

Coronavirus (COVID-19) changed the view of people towards life in all the countries of the world in December 2019. The virus has made chaos that cannot be predicted. This problem requires using a variety of technologies to aid in the identification of COVID-19 patients and to control the disease spread. For suspected instances of COVID-19 disease, chest X-ray (CXR) imaging is a standard with fewer costs, but it does not need a COVID-19 examination approach without using technology to help for a suitable diagnosis. In response to this issue, a big dataset of CXR images was divided into four classes found on the website Kaggle. Dealing with large data of the images needs dataset reprocessing through choosing the optimal method for getting speed and best accuracy. Dataset reprocessing converts into gray level then adjust image intensity, resize and extract the best features then apply Machine Learning ML models. The use of different prediction models, ML algorithms, and their performances are calculated with evaluation on the dataset after reprocessing. Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and K-Nearest Neighbors (KNN) are models used to foretell the specialized who would be diagnosed with COVID-19 quickly by using CXR images classification. The KNN has revealed the best accuracy compared with the others such as GNB, DT, SGD, LR, and RF. Also, KNN has the best-weighted average for all parameters, which are precision, sensitivity, and F1-score compared with the other models. © 2022 IEEE.

Prediction of Happiness Score of Countries by Considering Maximum Infection Rate of People by COVID-19 using Random Forest Algorithm

In this paper, the relationship between COVID-19 Maximum Infection Rate (MIR) and the happiness indicators has been investigated for the prediction of Happiness Score of Countries using Random Forest (RF) algorithm. The per-formance of the proposed algorithm is also compared against five other algorithms such as Linear Regression (LR), Ada Boost Classifier (ABC), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (NB) and Logistic Regression. The comparison of performance includes parameters like training accuracy, testing accuracy and computation time. It is clear from the observation that the proposed approach is superior to others. Then the parameters like MAE, MSE, RMSE, R2 Score, Adjusted R2 Score is calculated. This proposed algorithm can be used for other classification and regression work involving large amount of data with missing values like COVID- 19 datasets. © 2022 IEEE.

A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of the aforementioned techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin. © 2022 IEEE.

Performance Comparison of Multiple Supervised Machine Learning Algorithms for COVID-19 Mortality Prediction

The coronavirus disease (COVID-19) has wreaked havoc on populations around the world. Every day, thousands of people are dying as a result of this lethal virus. Patients with pre- existing conditions, as well as the elderly, are more susceptible to the disease. Artificial intelligence can play a vital role to track patient health conditions using various parameters. It assists in determining how to best handle certain patients in order to save their lives. The various parameters of a patient's health condition may have a significant impact on the outcome. Various artificial intelligence strategies are a blessing in minimizing the loss from COVID-19. This paper focuses on predicting the potential outcome of a patient using the COVID-19 dataset obtained from John Hopkins University of infected patients, which will help minimizing the death toll of COVID-19 disease. In this study, the performance of various machine learning models is compared for predicting COVID-19-affected patient's mortality using Logistic Regression, Support Vector Machine, K Nearest Neighbor, Decision Tree and Gaussian Naive Bayes. Finally, the best model for hyper parameter tuning was chosen from the comparative section. After hyper parameter optimization, a maximum accuracy of 95 percent and an F1 score of 89 percent using the K Nearest Neighbor algorithm was achieved. © 2022 IEEE.

Intelligent ICH Detection Using K-Nearest Neighbourhood, Support Vector Machine, and a PCA Enhanced Convolutional Neural Network

Stroke is a critical condition with excessive mortality rate. The risk is largely from intracranial haemorrhage, and the primary causes are elevated blood pressure and trauma. Identification of haemorrhage is time critical, and it affects clinical management. Non-contrast computed tomography scans are pragmatic in disease confirmation and require the efforts of an expert radiologist. The impact of COVID-19 creates an extra burden on stroke care. We propose to develop an intelligent intracranial haemorrhage detection algorithm using K-nearest neighbourhood and support vector machine. The algorithm reported an accuracy of 85 and 87.5%. Further, we implemented a principal component analysis enhanced convolutional neural network (PCA-CNN) model that classified haemorrhage and normal subjects. The models achieved a sensitivity, specificity, and F1-score of 1.0, 0.91, and 0.95, respectively, for CNN and 1.0 each for PCA-CNN. We believe that our model can assist the radiologist in the clinical diagnosis of intracranial haemorrhage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Machine Learning Performances for Covid-19 Images Classification based Histogram of Oriented Gradients Features

Coronavirus disease (Covid-19) is an infectious disease that attacks the respiratory area caused by the severe acute respiratory syndrome (SARS-CoV-2) virus. According to the World Health Organization (WHO) as of April 2022, there were more than 500 million cases of Covid-19, and 6 million of them died. One of the tools to detect Covid-19 disease is using X-ray images. Digital X-ray images implementation can be developed classification method using machine learning. By using machine learning, the diagnosis of this disease can be faster. This study applied a features extraction method using the Histogram of Oriented Gradients (HOG) algorithm and the Linear Support Vector Machine (SVM), K-Nearest Neighbor (KNN) Medium and Decision Tree (DT) Coarse Tree classification methods. The study can be used in the diagnosis of Covid-19 disease. The best method among the classification methods is features extraction from HOG algorithm and DT Coarse Tree. The highest values of accuracy, precision, recall, specificity, and F-score were 83.67%, 96.30%, 78.79%, 98.25, and 76.48%. © 2022 IEEE.

A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning

Effective screening helps for quick and accurate detection of COVID-19 and it also decreases the burden on the healthcare system. Prediction models with numerous criteria have been developed to estimate the probability of infection. These are designed to assist medical workers across the world in triaging victi ms, especially in places with limited medical resources. For predicting the COVID-19 using symptoms, the dataset is taken from the website of the Israeli Ministry of Health. The dataset contains 9 attributes and 2, 78, 848 samples. The raw dataset is cleaned using pre-processing techniques. The Machine learning algorithms like Random Forest, K Nearest Neighbor, Decision Tree, and hybrid Random Forest, K Nearest Neighbor, and Decision Tree are applied on the 1, 95, 194 samples to identify the model. The predicted model is tested on 83, 654 samples to ensure the quality of the designed model. The performance metrics like ROC [Receiver Operating Characteristic] curve, True Positive and Negative Rate, False Positive and Negative Rate, Positive and Negative Predictive Value, and Accuracy are applied to check the model. From the evaluation result, the proposed hybrid model gives high accuracy of 98.97%. The proposed technique might be utilized to priorities COVID-19 screening when testing capabilities are constrained., among several other things. © 2022 IEEE.

Analysing the Twitter Sentiments in COVID-19 Using Machine Learning Algorithms

The coronavirus emanated in Wuhan city of China, in the last month of 2019 and was even announced as a global threat. Social media could be an utterly noteworthy supply of facts during a time of crisis. User-generated texts yield perception into users' minds withinside the direction of such times, giving us insights into their critiques in addition to moods. This venture examines Twitter messages (tweets) regarding people's sentiment on the unconventional coronavirus. The essential aim of sentiment evaluation is the origin of human emotion from messages or tweets. This venture is geared toward using numerous gadgets studying type algorithms to expect the people's reception of the worldwide pandemic by reading their tweets on Twitter. In the course of this paper, we are testing our dataset on five different classifiers, namely Random Forest, Logistic regression, Multinomial naive Bayes, K-nearest neighbor, and Support vector machines classifiers. Together with precision rankings and balanced accuracy rankings, metrics are offered to gauge the fulfilment of the numerous algorithms implemented. The K-Nearest Neighbor classifier has given the highest precision score while the Logistic Regression classifier gives the highest recall, F1, accuracy and balanced accuracy scores. © 2022 IEEE.