Prediction of Stable Angina for Coronary Artery Diseases using Boosting Algorithm

Narrowed arteries block the blood flow to the heart muscle and other parts of the body, which can cause chest pain. Coronary arteries disease (CAD) can weaken the heart muscle causing heart failure, in which the heart cannot pump blood. A person with underlying diseases is more prone to get highly affected by COVID-19 because of the decreased immunity. Cardiovascular disease and coronary heart disease have been associated with worsened outcomes of COVID-19 patients. Thus, detecting CAD at a proper stage is crucial to avoid any further serious issues. This paper is an empirical analysis to predict stable angina for CAD using Histogram gradient boosting (HGB) and Adaboost (ADB) classifier algorithm and compared the performance with traditional Naïve Bayes (NB) algorithm. © 2023 IEEE.

Benign and Malignant Skin Lesion Detection from Melanoma Skin Cancer Images

Skin cancer is the most dangerous and lethal cancer that affects millions of people each year. The accurate identification of skin cancers can not be accomplished without expert dermatologists. However, specific research studies of WHO in Canada, US and Australia, show that in the year 1960s to 1980s, the cases of skin cancer has noted more than two times increased in comparison with the previous years. The identification of skin cancer in its early stage is an expensive and difficult task because it doesn't cause too much bad in the initial phase. Whereas, the growth of skin cancer requires biopsy and many other treatments each time which is quite costly as per the statistics of India. This challenge makes it a necessary step to identify the existence of skin cancer in the early stages to increase immortality. With the evolution and progression in technology, there are various methods which have participated in and solved medical issues including covid19, pneumonia and many others. Similarly, machine learning(ML) and deep learning(DL) models are applicable to diagnosing skin cancer in its early stages. In this work, the support vector machine (SVM), naive bayes (NB), K-nearest neighbour (KNN) and neural networks(NN) have been used for classifying benign and malignant lesions. Furthermore, for the feature extraction from the dataset, a pre-trained SqueezeNet model has been used. The classification results of KNN, SVM, NB and NN have been shown in the accuracy, recall, F1-Measure, precision, AUC and ROC. The comparison of the models has resulted that the NN model outperforms all other models when applied with the SqueezeNet feature extractor with the highest accuracy, F1-Measure, recall, precision and AUC as 88.2%, 0.882, 0.882, 0.882 and 0.957, respectively. Lastly, the performance metrics analogies results of each model have been illustrated for the classification of benign and malignant lesions. © 2023 IEEE.

Classification of Chest X-Ray Images for COVID-19 Positive Patients Using Transfer Learning

The Coronavirus pandemic COVID-19 which has been declared as a pandemic by the World Health Organization has infected more than 212,165,567 and fatality figure of 4,436,957 as of 22nd August 2021. This infection develops into pneumonia which causes breathing problem;this can be detected using chest x-rays or CT scan. This work aims to produce an automated way of detecting the presence of COVID-19 infection using chest X-rays as a part of transfer learning strategy to extract numerical features out of an image using pre trained models as feature extractors. Then construct a secondary data set out of these features, and use these features which are simple numerical vectors represented in tabular form as an input to simple machine learning classifiers that work well with numerical data in tabular form such as SVM, KNN, Logistic regression and Naive Bayes. This work also aims to extract features using texture-based techniques such as GLCM and use the GLCM to obtain 2nd order statistical features and construct another secondary data set based on texture-based feature extraction techniques on images. These features are again fed into simple machine learning classifiers mentioned above. A comparison is done, between deep learning feature extraction strategies and texture-based feature extraction strategies and the results are compared and analyzed. Considering the deep learning strategies Mobile Net with SVM perform the best with 0.98 test accuracy, followed by logistic regression, KNN and Naive Bayes algorithm. With respect to GLCM feature extraction strategy, KNN with test accuracy with 0.96 performed the best, followed by logistic regression, SVM and naive Bayes. Overall performance wise deep learning strategies proved to be effective but in terms of calculation time and number of features, texture-based strategy of GLCM proved effective. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Detection in the Images of Chest CT Scan Using K-NN in Comparison with NB Classifier to Improve the Accuracy

This chapter is about the improvisation in the accuracy in COVID-19 detection using chest CT-scan images through K-Nearest Neighbour (K-NN) compared with Naive-Bayes (NB) classifier. The sample size considered for this detection is 20, for group 1 and 2, where G-power is 0.8. The value of alpha and beta was 0.05 and 0.2 along with a confidence interval at 95%. The K-NN classifier has achieved 95.297% of higher accuracy rate when compared with Naive Bayes classifier 92.087%. The results obtained were considered to be error-free since it was having the significance value of 0.036 (p < 0.05). Therefore, in this work K-Nearest Neighbor has performed significantly better than Naive Bayes algorithm in detection of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Novel Classification Technique for Safety Measures on Covid-19 Using Featured-Based Sentimental Analysis

Covid-19 is a term that has frightened the globe because it has broken beyond socioeconomic barriers in which people literally forgot the word social help because of this deadliest virus.The main goal of this study is to create a model that forecasts Covid-19 reviews based on coronavirus ratings from Kaggle repository. The World Health Organization(WHO) declared a pandemic of the coronavirus infection when it first appeared in 2019. People are worrying and concerned about their health as the number of instances rises throughout the world. People's physical and emotional health is inversely proportional to the pandemic scenario. As a result, in this case, a categorization model based on numerous metrics is required to rescue nations by analyzing facts and information about the outbreak. In this article to organise the reviews or opinions provided by people worldwide, we performed emotional or opinion classification using a Novel classifier. Then, the accuracy of the proposed model is compared with existing base classifiers like NB(Naive-Bayes) and Support Vector Machine(SVM), where Novel classifier gave the best accuracy compared to the other two classifiers, i.e., 95 © 2022 IEEE.

Local Naïve Bayes for Predicting Evolution of COVID-19 Patients on Self Organizing Maps

The most recent Clinical Decision Support Systems use the potential of Machine Learning techniques to target clinical problems, avoiding the use of explicit rules. In this paper, a model to monitor and predict the risk of unfavourable evolution (UE) during hospitalization of COVID-19 patients is proposed. It combines Self Organizing Maps and local Naïve Bayes (NB) classifiers because of interpretation purposes. We used the results of six blood tests (leukocytes, D-dimer, among others) provided by a Spanish hospital group. The probabilistic approach allows us to get the daily risk of UE for each patient in an interpretable way. Several variants of the NB classifiers family have been explored, mainly weighting and likelihood estimation (parametric and nonparametric). Despite the over-simplified assumptions of the NB classifiers, they provided good predictive results in terms of sensitivity and specificity. The model with nonparametric likelihood estimation provided the best risk prediction over time even when designed with a limited number of samples. Specifically, the median value and interquartil range for the risk prediction were quite reliable even 10 days before the event day for patients hospitalized longer than 7 days. The risk median values also agree with the gold-standard for patients with a hospital stay shorter than 7 days, though the interquartil range can be too wide (probably because of the variability in the inpatient days - sometimes, just 2 days). Though a deepest analysis considering more patients and features would be convenient, our results show the potential of the proposed approach, both from a technical and clinical viewpoint. © 2022 IEEE.

Analysis of Depression, Anxiety, and Stress Chaos Among Children and Adolescents Using Machine Learning Algorithms

In recent times, the pandemic seems to have a serious impact on the mental health of people around the world across all age groups. This has been manifested in the form of unstable mental conditions, depression, anxiety, stress, and many other similar mental illnesses among individuals. In this study, we explore the use of machine learning classification algorithms to detect and classify children and adolescents with unstable mental conditions such as depression, stress, and anxiety through the Covid-19 period based on demographic information and characteristics using the DASS-21 Scale. Using a dataset of 2050 Chinese participants, an attempt has been made to classify their depression, stress, and anxiety behavior into different levels (Normal, Moderate, and Severe). The classification algorithms considered are Support Vector Machines, KNN, Naive Bayes, and Decision Trees. It is observed that the Support Vector Machine is the most effective method for the classification of mental depression, anxiety, and stress conditions. The goal of the study is to build a classification model for accurate categorization of unknown samples into appropriate psychological chaos levels. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Detection of COVID-19 Infection from Clinical Findings Using Machine Learning Algorithm

COVID-19 infection is a transmissible virus causing acute respiratory syndrome spreading worldwide. The number of patients infected by this deadly virus increases steadily, causing a high mortality rate. Hence, it is crucial to diagnose and identify the COVID-19 infection for earlier treatment of the patients. This study has applied four algorithms, namely, Logistic Regression (LR), Nu-Support Vector Machine (Nu-SVM), Multi-layer perceptron (MLP) and Naive Bayes (NB) to identify COVID-19 infection. The clinical laboratory findings of 600 individuals were taken from Hospital Isrelita Albert Einstein, Sao Paulo, Brazil, used in this study. We have selected significant features using Random forest-based recursive feature elimination for predicting the infection. Experiments are conducted with 90% training and 10% testing data. The performance result shows that the Nu-SVM algorithm obtained the prediction accuracy of 95% with 100% sensitivity and 94.23% specificity in predicting the infection. To our knowledge, the result achieved by Nu-SVM is the highest in the literature. Hence, the model can be used as a tool for the initial prediction of COVID-19 disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Design and Implementation of Remote Health Monitoring System and Application for Priority Recognition Using Machine Learning

This work demonstrates a remote health monitoring system that provides a holistic perspective of cases and their health conditions. Remote Patient Monitoring (RPM) systems will play a conspicuous role in the millennium of medical management. In this paper, to monitor covid patients during their quarantine days to keep track of chronic circumstances. For that, the model of a non-reactive preference grading independently in a single device to collect the essential parameters like blood Oxygen level, temperature and pulse rate. To predict and conduct the priority division using supervised machine learning algorithm for the received medical packets and relay them according to their priorities. This hitch results in transmitting advanced significance data packets of high importance in an advanced average waiting time. In this design, to acknowledge a vital criterion distinguishing the priority of health-info carried by a file and other low-ranking digital data parcels of different cases. The stored data then given for the supervised machine learning classification algorithms. In that the better accuracy of priority classification of 93.5% obtained from support vector machine (SVM) algorithm outperforms than the other machine learning classifiers and are 91%, 88%, 89% with respect to Multilayer Perception(MLP), Baysian Network (BN) and Logisitic Regression(LR). © 2022 IEEE.

Sentiment Analysis on MySejahtera Application during COVID-19 Pandemic

MySejahtera is an application developed by the Malaysian government to assist and monitor the Covid-19 flare-up that happened in Malaysia by allowing users to assess their health risk in the event of an outbreak. In MySejahtera, the public obtains significant information about the affected areas and other health-related conditions. The use of MySejahtera became mandatory during the pandemic to reduce the Covid-19 spread. This paper reports the public sentiment towards MySejahtera using the supervised Machine Learning (ML) method. The total number of feedback scraped from Twitter, Google Play Store, and App Store was 33 376, and the modelling was trained using 20 647 feedbacks based on the timeline during the launch of the application;26th March 2020 until 25th November 2021. The corpus was used to determine the feedback label, either positive or negative. The tested ML algorithms are Support Vector Machine (SVM) and Naïve Bayes (NB) through evaluation metrics that are accuracy, precision, recall/sensitivity, and specificity. The result of modelling shows that the SVM classifier with 90:10 percentage split, using Vader extraction technique, has the highest accuracy of 89.93% and recall/sensitivity of 90.55%. The results of this sentiment analysis are visualized for better understanding. It is preferable to utilize a Malay Language corpus and to have more records from Twitter for future work. © 2022 IEEE.

Public Sentiments in User Generated Content amid COVID-19 Pandemic in Ghana

Towards post COVID-19 pandemic, a natural language processing (NLP) technique was leveraged to understand the sentiments of Ghanaians through their public discourse in tweets during the lockdown period in Ghana. With NLP resources, feature words were extracted from the tweets and fed into three machine learning algorithms to track public sentiments in the tweets. The algorithms, support vector machines (SVM), naïve-bayes (NB) and artificial neural network (ANN) were evaluated to ascertain their efficacies. Frequently occurring words used by Ghanaians during the lockdown period were extracted to provide more insight into public sentiments. The study revealed that negative sentiments prevailed throughout the COVID-19 lockdown among Ghanaians. However, positive sentiments were surprisingly high at some points during the lockdown period. The result of evaluating the machine learning classifier yielded SVM as the best performing classifier though the other classifiers performed beyond the acceptable threshold. With these findings, it is envisioned that this study will be adopted by policymakers, as a guide, towards public management of public sentiments in pandemics. © 2022 IST-Africa Institute and Authors.

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.

Inclusive Study of Fake News Detection for COVID-19 with New Dataset using Supervised Learning Algorithms

Covid-19 imposes many bans and restrictions on news, individuals and teams, and thus social networks have become one of the most used platforms for sharing and destroying news, which can be either fake or true. Therefore, detecting fake news has become imperative and thus has drawn the attention of researchers to develop approaches for understanding and classifying news content. The focus was on the Twitter platform because it is one of the most used platforms for sharing and disseminating information among many organizations, personalities, news agencies, and satellite stations. In this research, we attempt to improve the detection process of fake news by employing supervised machine learning techniques on our newly developed dataset. Specifically, the proposed system categorizes fake news related to COVID-19 extracted from the Twitter platform using four machine learning-based models, including decision tree (DT), Naïve Bayes (NB), artificial neural network (ANN), and k-nearest neighbors (KNN) classifiers. Besides, the developed detection models were evaluated on our new dataset, which we extracted from Twitter in a real-time process using standard evaluation metrics such as detection accuracy (ACC), F1-score (FSC), the under the curve (AUC), and Matthew's correlation coefficient (MCC). In the first set of experiments which employ the full dataset (i.e., 14,000 tweets), our experimental evaluation reported that DT based detection model had achieved the highest detection performance scoring 99.0%, 96.0%, 98.0%, and 90.0% in ACC, FSC, AUC, and MCC, respectively. The second set of experiments employs the small dataset (i.e., 700 tweets);our experimental evaluation reported that DT based detection model had achieved the highest detection performance scoring 89.5%, 89.5%, 93.0%, and 80.0% in ACC, FSC, AUC, and MCC, respectively. The results obtained for all experiments have been generated for the best-selected features. © 2022, International Journal of Advanced Computer Science and Applications. All rights reserved.

Review of ML techniques for analyzing Novel Corona Virus

Currently, the demand of machine learning is increasing in healthcare field for disease diagnosis. The various kinds of machine learning algorithms are helping the medical field for prognosis of diseases with accuracy and therefore serving the humankind in timely classification and detection of diseases. This study emphasizes on using different machine learning techniques for analysis of Covid-19 disease prediction. This paper presents the review of several machine learning classifiers such as SVM, Ensemble learning, Multilayered Perceptron, Naive Bayes, KNN and ANN, and analyze their classification accuracies in Novel Corona Virus prediction. The authorized datasets have been considered to perform this analysis. This analysis may serve as good indicator for analysts and medical professionals in selection of efficient classifier for the datasets that may save the time and prediction cost. © 2022 IEEE.

Student Performance Based on Student Final Exam Prediction

The Covid-19 pandemic situation has made changes to the education system. Educational institutions carried out the shift from the face-To-face learning model to the distance learning model to adapt to the pandemic situation to maintain educational activities' sustainability. Despite changes in learning models, education providers certainly want to maintain academic quality by producing graduates with superior academics, practical knowledge, and innovative thinking. The problem currently faced is how education providers can monitor students' performance to complete their studies correctly. Therefore, a grade prediction is needed that helps students, lecturers, and administrators of educational institutions maintain and improve academic quality. This study compares the techniques. This study shows that the Naïve Bayes method provides a higher level of accuracy than the KNN method, which is 96%. © 2021 IEEE.

Identification of Optimal Model for Multi-Class Classification of COVID Tweets

Covid-19 is the worst-hit pandemic that has affected humankind to date. It sent all major nations around the globe into lockdowns for at least half of 2020. The lockdown started to increase unrest in the population, and even some of them started sharing the emotions infused by the unrest and lockdown over social media platforms in the form of posts, stories, articles. The emotions that underlie those posts can be categorized into three categories positive, negative and neutral, and the individual posts can be classified into respective labels. We considered one of the social platforms' Twitter and collected Twitter tweets. The dataset included the text from the tweet along with emotion. The dataset was pre-processed, including removing stop words from the dataset, stemming and lemmatizing the words from tweets text. Our work focused on various models that can be used to analyze sentiment and classification. The work includes implementing standard classification models like Naive-Bayes multinomial Classifier, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree Classifier, Logistic Regression, Deep Learning models - Long short-term memory (LSTM), Gated recurrent unit (GRU), Bidirectional long-short term memory (Bidirectional LSTM), Bidirectional Encoder Representations from Transformers (BERT). The results from all these models are compared and tried to establish the most efficient model based on accuracy. The BERT model outperformed all other methods when compared to other models developed using Machine Learning (ML) and Deep Learning (DL) techniques. © 2022 Bharati Vidyapeeth, New Delhi.

Predicting Creditworthiness of Smartphone Users in Indonesia during the COVID-19 pandemic using Machine Learning

In this research work, we attempted to predict the creditworthiness of smartphone users in Indonesia during the COVID-19 pandemic using machine learning. Principal Component Analysis (PCA) and Kmeans algorithms are used for the prediction of creditworthiness with the used a dataset of 1050 respondents consisting of twelve questions to smartphone users in Indonesia during the COVID-19 pandemic. The four different classification algorithms (Logistic Regression, Support Vector Machine, Decision Tree, and Naive Bayes) were tested to classify the creditworthiness of smartphone users in Indonesia. The tests carried out included testing for accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristics (AUCROC) assesment. Logistic Regression algorithm shows the perfect performances whereas Naïve Bayes (NB) shows the least. The results of this research also provide new knowledge about the influential and non-influential variables based on the twelve questions conducted to the respondents of smartphone users in Indonesia during the COVID-19 pandemic. © 2022 IEEE.

COVID-19 Fake News Detection System

This article deals with the problem of the rapidly increasing COVID-19 infodemic in the world. Thus, there is a need for an effective framework of detecting fake information or misleading news related to COVID-19 virus/disease. To resolve this, we have used a dataset obtained from ConstraintAI'21. The dataset consists of 10,700 tweets and online posts of fake and real news concerning COVID-19. Machine Learning (ML) algorithms compared in this paper to classify the given news or tweet into real or fake are Logistic Regression (LR), K-Nearest Neighbor (KNN), Linear Support Vector Machine (LSVM), Random Forest Classifier (RFC), Decision Tree (DT), Naive Bayes (NB) and Stochastic Gradient Descent (SGD) algorithm. Two feature extraction techniques were used count vectorization and TF-IDF. Deep Learning (DL) algorithms implemented using Adam optimizer are Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The best testing accuracy was achieved with the LSVM model using TF-IDF feature extraction method followed by Stochastic Gradient Descent classifier with TF-IDF feature extraction technique. LR, DT, and RFC performed better with the Count vectorization feature extraction technique, whereas LSVM, KNN, NB and SGD had better accuracy with TF-IDF feature extraction technique. The LSTM model performed slightly better among the DL algorithms. © 2022 IEEE.

A Machine Learning Approach to Predict Renal Diseases with SARS-CoV-2

Research has shown that up to a lot of people hospitalized with COVID-19 get an intense kidney injury. In some serious cases, Kidney failure occurs suddenly without any major symptoms that are totally unpredictable to identify in the early stage. The reason behind that we have a lack of knowledge and experience regarding this. The main purpose of our research is to develop a framework that will assist individuals with foreseeing the danger of constant renal sickness growing rate after being infected with COVID-19. Here we have utilized 773 raw data and trained them and we have also taken care of our missing data. In this paper, we have used KNN, Naïve Bayes, ANN model and Ant Colony Optimization (ACO) for making the system ready for assumption. We have carried out these calculations in the python language. The exactness that we acquire by utilizing KNN calculation is 95%, Naïve bayes is 98.30% ANN is 97.5% and Ant Colony Optimization (ACO) is 95.5% separately which is generally outstanding. By utilizing our proposed strategy, prediction of renal diseases after COVID-19 in the beginning phase will be conceivable. All the data are collected from our neighborhood medical clinic. This research has shown us the current situation in this COVID-19 pandemic with regards to Chronic Kidney Sickness which is known as renal disease. © 2021 IEEE.

Machine Learning Applications in Decision Intelligence Analytics

Writing software (programs) is an obstruction, we do not have so many good developers who can develop much more enhanced models and so, for this purpose today many use the data instead of people to perform the same task. According to the generations’ needs, the programmers developed the machine learning approach to make the programming much more scalable and expandable in this domain. Before, machine learning traditional programming is a much more famous approach where programmers used to code each and every single line with their own and its main drawback is that it is not so much scalable. Here, in this chapter we are going to discuss various applications of machine learning and the algorithms they are using along with their advantage, disadvantage, and its working model of how much the particular application is scalable. Like we are going to discuss virtual personal assistants, email spam, online fraud detection, traffic predictions, social media personalization, and many more. In coming to algorithms, we will get to know about Naive Bayes algorithms, neural networks, KNN algorithms, linear regression model, logistic regression model, etc. On coming to today’s need we are also going to discuss its applications in detection of COVID-19 defaulters by the use of semantic segmentation algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.