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 Approach for Predicting Covid 19 Recovered Cases using Nourishment by Comparing KNN over SVM

To predict the accuracy value of COVID19 recovered number of patients using Nourishment. Material and Methods: For forecasting accuracy percentage of COVID19 recovered patient health diet, Novel K Nearest Neighbour with test size (N=10) and Support Vector Machine with test size (N=10) were iterated 20 times to COVID19 recovered number of patients with g power as 80 %, threshold 0.014 and confidence interval as 95%. Sigmoid function is used in K Nearest Neighbour prediction to probability to help enhance accuracy. Results: In comparison to Support Vector Machine 66% percent Accuracy, Novel K Nearest Neighbour produced substantial results with 94 % Accuracy. Support Vector Machine and K Nearest Neighbour statistical significance is p=1.000(p<0.05) Independent sample T-test value states that the results in the study are significant. Conclusion: KNN is a straightforward and efficient algorithm for quickly building Models of machine learning. KNN predicting COVID19 Health Diet % with more accuracy. © 2022 IEEE.

Detection of COVID-19 Patients using Speech Recognition with Support Vector Machine' and Comparing with 'K Nearest Neighbour Algorithm'

This research endeavor is focused on identifying patients with the Covid-19 virus via the use of a novel voice recognition technique that makes use of a Support Vector Machine (abbreviated as 'SVM') and compares its accuracy with that of 'K-Nearest Neighbor' (abbreviated as 'KNN'). When it comes to speech recognition, the SVM method is regarded to be group 1, and the KNN method is considered to be group 2, and both groups have a total of 20 samples. The outcomes of these data were analyzed using statistical analysis using a'independent sample T-test,' which has a margin of error of 5% and a pretest power of 80%. At a significance of 0.042 (p 0.05), KNN obtains an accuracy of 87.5% whereas SVM achieves an accuracy of 96.5%. As compared to KNN, the prediction accuracy of Covid-19 employing SVM in novel voice recognition achieves much higher levels of accuracy. © 2023 IEEE.

Social Media Sentiment Analysis on Third Booster Dosage for COVID-19 Vaccination: A Holistic Machine Learning Approach

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Gabor Filter-Based Caries Image Feature Analysis Using Machine Learning

Dental caries sufferers in Indonesia demonstrate a higher frequency than other dental diseases even before the Covid-19 pandemic. The high risk of spreading the virus during the pandemic hinders handling dental care patients. Teledentistry is suggested as the main alternative to reduce the risk of spreading the virus. This study aims to establish a system for classifying the level of dental caries based on texture applicable for clinical implementation. Dental caries images were extracted using the Gabor Filter method and classified using the Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). A downsampling technique was applied to this system to reduce the large number of features affecting the classification time. System testing revealed that the Cubic SVM model generated the best result: Accuracy of 90.5%, precision of 89.75%, recall of 89.25%, specificity of 91.75%, and f-score of 88.5%. © 2022 IEEE.

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.

Improved Accuracy in Speech Recognition System for Detection of Covid-19 Using K Nearest Neighbour and Comparing with Artificial Neural Network

The objective of this research is to recognize the speech signals for identifying the Covid-19 using K Nearest Neighbour (KNN) and comparing accuracy with an Artificial Neural Network (ANN). Speech recognition using KNN is considered as group 1 and Artificial Neural Network is considered as group 2, where each group has 20 samples. ANN is a machine learning program in which the input is processed by numerous elements and produces the output based on predefined functions. KNN is defined to find the relations between the query and pick the value closest to the query. These groups were analyzed by an independent sample T-test with 5% of alpha, and 80% of pretest power. ANN and KNN achieve an accuracy of 83.5% and 91.49% respectively (significance < 0.05). This analysis observed that KNN has significantly higher accuracy than ANN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on KNN-Based GNSS Coordinate Classification for Epidemic Management

As epidemics such as COVID-19 and monkeypox spread, tracing specific people with restricted activities (targets) within administrative areas (targeted areas) is an effective option to slow the spread. Global Navigation Satellite Systems (GNSS) that can provide autonomous geospatial positioning of targets can assist this issue. K-nearest neighbors (KNN) is one of the most widely used algorithms for various classifications or predictions. In this paper, we will use the technique of KNN to classify the areas of the targets and explore the relationship between the density of targets to a area and the accuracy of classifications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Early Detection of Covid-19 Through Cough Sound Recognition using LPC and K-NN algorithm

COVID-19 (Coronavirus Disease 2019) is an infectious disease caused by the SARS-CoV-2 virus. This disease has spread worldwide since the beginning of 2020. Patients with this highly contagious disease generally experience only mild to moderate respiratory problems such as sore throat, cough, runny nose, fever, shortness of breath, and fatigue. However, some will become seriously ill and may cause severe respiratory distress or in severe cases multiple organ failure. Therefore, early identification of COVID-19 patients is very important. In this study, a disease detection system was created using an open dataset from COUGHVID which were contained the coughing sound of the Covid-19 disease. The implementation of the cough voice recognition system uses the K-Nearest Neighbor (K-NN) machine learning method and the Linear Predictive Coding (LPC) as method of extracting features from voice. The system was built using the Raspberry Pi 3 b+ microcontroller with microphone voice input and connected to a 3.5-inch LCD touchscreen display as the interface of the system device. The test uses a coughing sound as input through a microphone and processed by LPC feature extraction. At each running process, about 399 MB of memory is used from a total of 1 GB of memory. Meanwhile, the prediction of coughing sounds with the K-NN classification algorithm using 5 neighbors produces accuracy of 62% to predict disease. © 2022 ACM.

Texture-Based Covid-19 Images Detection System using Haar Wavelet Transformation Algorithm

Covid-19 remains the worldwide highlight because it is still growing rapidly and has greatly impacted human activities. Preventing its transmission by detecting to allow other actions to be taken continues to be carried out. Various research efforts have been performed to detect Covid-19. Along with developing its detection, technology can be conducted by image processing or machine learning. The detection in this study was carried out using X-ray images of Covid-19 positive people, totaling 101 images, propagated through pre-processing to 404 images. Then, these images were compared with the X-ray images of normal people amounting to 202 and the X-ray images of pneumonia-positive people totaling 390. The extraction process was performed using the Haar wavelet transformation by classifying the data using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) methods. The Fine KNN model obtained the best accuracy with an average of 94.66%. © 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.

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.

Risk Analysis and Prediction of Covid-19 Infection Using Machine Learning Techniques

Coronavirus (COVID-19), the lethal contagious virus which has caused a pandemic, has metastasized all over the world starting from China. The figures observed of the number of casualties, is in millions and billions. This new malicious virus has caused panic amongst pubic, implanted fear and number of doubts in people's minds. There is lack of information as scientists are working on eradicating this deadly virus, less information has instilled doubts and people are panicking being helpless about how to cope up with the virus. Ways to protect oneself from getting infected, how could and where could one seek medical help when needed, these kinds of queries should be sorted out and the public needs to be educated about the virus. This will help calm down the public. This would also aid in keeping tranquil environment and even help in health and government sector workers to carry on with their duties without any obstacles. © 2022 WCSE. All Rights Reserved.

BaRCODe: A Blockchain-based Framework for Remote COVID Detection for Healthcare 5.0

The emergence of wearable technology for assessing health data has revolutionized the health sector. Consequently, medical practitioners can now virtually examine the patient's health and provide immediate medications. However, when the security of this equipment is considered, there is a grave hazard. The data is delivered across an open channel, i.e., the internet, from the patient's device to the doctor, and it may be tampered with by intruders. Insurance firms keep a record of their patient's health and subsequently offer appropriate treatments. In case of data tampering, the insurer will consider the conduct fraudulent. As a result, ensuring the system's integrity and granting access only to authorized stakeholders becomes critical. Blockchain has surpassed conventional technologies in terms of guaranteeing security for information held. Motivated by these, this paper has developed a novel approach that uses blockchain technology to transmit a patient's health information to a medical expert. Machine Learning (ML) techniques, K-Nearest Neighbours (KNN), and Logistic Regression (LR) is used to categorize Coronavirus Disease (COVID) positive users and malevolent wearable devices, respectively. The performance of the proposed model is evaluated considering parameters such as accuracy, precision, recall, and F1 score. The proposed model achieves an accuracy of 98.15% for COVID positive detection and 96.78% for malevolent user detection. © 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.

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.

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.