Covid-19 and Diabetes Risk Prediction for Diabetic Patient using Advance Machine Learning Techniques and Fuzzy Inference System

Diabetes has become a common and critical disease which generally occurs due to the presence of high sugar in blood for long time. A diabetic patient has to follow different rules and restrictions where he/she has to be under proper attention by measuring diabetes level frequently to avoid unexpected risk. The risk become more when patient even doesn't know that he/she is already having diabetes and doesn't follow those restrictions. To prevent this risk, everyone should check the diabetes status to be sure. With the same target different system using machine learning techniques have been introduced which can predict the diabetes status of a patient. But the challenging fact is that the performances and accuracy of those models are questionable where there may be a huge risk of patient's life. The conventional systems are not able to show that which level of diabetes a patient can have using the previous records. To solve this issue, through this paper an efficient system has been proposed with which the diabetes status can be predicted correctly. The proposed system can also show the complexity of diabetes as well as the Covid-19 risk percentage that can also be possible to measure. After comparing several machine learning techniques, the suitable model has been selected where high level of accuracy has been ensured in term of predicting the disease. © 2022 IEEE.

Construction and Application of Bid Fraud Prediction Model Based on AdaBoost Algorithm

In recent years, with the development of Internet big data technology and e-commerce platform, many active offline transaction methods have gradually shifted to online. Online auctions have come a long way due to COVID-19, but bidding fraud has seriously disrupted the health of the industry. In this paper, the AdaBoost model is used to build a bidding fraud prediction model, and the prediction performance of the model is verified by data experiments, and it is found that it has a high accuracy for identifying bidding fraud. At present, there are few prediction models for bidding fraud, and it has broad development prospects. © 2022 IEEE.

SARS-CoV-2 Mortality Risk Prediction Using Machine Learning Algorithms to Aid Medical Decision-Making

The coronavirus has affected the world in every possible aspect such as loss of economy, infrastructure, and moreover human life. In the era of growing technology, artificial intelligence and machine learning can help find a way in reducing mortality so, we have developed a model which predict the mortality risk in patients infected by COVID-19. We used the dataset of 146 countries which consists of laboratory samples of COVID-19 cases. This study presents a model which will assist hospitals in determining who must be given priority for treatment when the system is overburdened. As a result, the accuracy of the mortality rate prediction demonstrated is 91.26%. We evaluated machine learning algorithms namely decision tree, support vector machine, random forest, logistic regression, and K-nearest neighbor for prediction. In this study, the most relevant features and alarming symptoms were identified. To evaluate the results, different performance measures were used on the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Risk Prediction and Contact Tracing

The emergence of the coronavirus COVID- 19 switched the limelight onto digital health technologies. To help the infection rates from surging, numerous governments are looking into applications that could help disrupt infection chains beforehand. We created a Self-Assessment Test using COVID Symptoms, that's capable of assessing the threat of COVID- 19 in the user using ML. The data also tracks the user and gives safety tips and recommendations. Using the Track Module, the user is notified of the nearby containment zones. The contact tracing module helps the user to maintain a specified distance from others. © 2022 IEEE.

The role of web browsing in credit risk prediction

Online mail order and online retail purchases have increased rapidly in recent years worldwide, with Covid-19 forcing almost all non-grocery shopping to move online. These practices have facilitated the availability of new data sources, such as web behavioural variables providing scope for innovation in credit risk analysis and decision practices. This paper examines new web browsing variables and incorporates them into survival analysis as predictors of probability of default (PD). Using a large sample of purchase and repayment credit accounts from a major digital retailer and financial services provider, we show that these new variables enhance the predictive accuracy of probability of default (PD) models at account level. This also holds in the absence of credit bureau data, therefore, the new information can help people who may not have a credit history (thin file) who cannot be assessed using traditional variables. Moreover, we leverage on the dynamic nature of these new web variables and explore their predictive value in short and long- term horizons. By adding macroeconomic variables, the possibility for stress-testing is provided. Our empirical findings provide insights into web browsing behaviour, highlight how the inclusion of non-standard variables can improve credit risk scoring models and lending decisions and may provide a solution to the thin files problem. Our results also suggest a direct value added to the online retail credit industry as firms should leverage the increasing trend of consumers embracing the digital environment. © 2022 The Authors

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Federated machine learning for privacy preserving, collective supply chain risk prediction

The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has been proposed to evaluate risk, in practice this does not happen due to privacy concerns. We propose a federated learning approach for collective risk prediction without the risk of data exposure. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This raises a second question: Under what circumstances would collective risk prediction be beneficial? We present an empirical case study where buyers predict order delays from their shared suppliers before and after Covid-19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers with limited datasets. Training data-imbalance, disruptions, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers with disproportionately larger order-books. We thus call for further research on on local and collective learning paradigms in supply chains. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

E-commerce fraud risk prediction based on RUSBoost algorithm

In the context of the era of big data, the emergence of e-commerce platforms has brought many opportunities and risks. Due to the COVID-19, e-commerce has achieved unprecedented development, and e-commerce fraud has severely damaged the healthy economic environment. This paper uses the RUSBoost algorithm to build an e-commerce fraud risk prediction model, and verifies the predictive performance of the model through data experiments. The results show that it has a high accuracy rate for identifying e-commerce fraud. If the model is applied to e-commerce, the losses caused by ecommerce fraud could be avoided in time. At present, there are fewer e-commerce fraud risk prediction models and have a wide development prospection. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

An Ensemble Machine Learning Based Approach for Health Risk Prediciton

chronic health risks have risen among young individuals due to several factors such as sedentary lifestyle, poor eating habits, sleep irregularities, environmental pollution, workplace stress etc. The problem seems to be more menacing in the near future, with the exacerbation of lifestyle conditions and unforeseen breakout of pandemics such as COVID-19. One possible solution is thus to design health risk prediction systems which can evaluated some critical features of parameters of the individual and then be able to predict possible health risks. As the data shows large divergences in nature with non-correlated patterns, hence choice of machine learning based methods becomes inevitable to design systems which can analyze the critical factors or features of the data and predict possible risks. This paper presents an ensemble approach for health risk prediction based on the steepest descent algorithm and decision trees. It is observed that the proposed work attains a classification accuracy of 93.72%. A simple graphic user interface has also been created for the ease of use and interaction and for prototype testing. © 2022 IEEE.

Efficient Disease Risk Prediction based on Deep Learning Approach

In recent years, the advancement of artificial intelligence (AI) and the progress of machine intelligence has allowed the people to perceive the great future of AI in the healthcare field. Deep learning technology has shown the promising results in early disease prediction. The performance of multi disease prediction has been improved dramatically due to progressive development from machine learning to deep learning technology. The most difficult task is accurate and early disease prediction. It aims to demonstrate the significant relationship between deep learning and healthcare industry mainly for early disease prediction. In this paper, deep learning based multi disease prediction such as diabetes, breast cancer and covid 19 detection are proposed and analysed. The selected deep learning models in this paper were ANN and CNN. These networks were chosen, as they contain only less number of layers than complex architectures like Densenet and Resnet model. Kaggle datasets are used for all three different diseases for efficient detection. The performance of deep learning classification algorithms is evaluated using a variety of evaluation metrics such as accuracy, precision, sensitivity and specificity. Our obtained results shows that ANN and deep CNN model achieves higher accuracy than existing machine learning models. Our proposed model has shown the greater accuracy of 73.37%, 96.49%, 96.66% in diabetes, breast cancer and covid-19 disease detection. © 2022 IEEE.

Early Mortality Risk Prediction in Covid-19 Patients Using an Ensemble of Machine Learning Models

COVID-19, which is subsequently named as SARS-CoV-2, First Human case was found in the City of Wuhan, from China, in Dec 2019. After that, the World health organization (WHO) has declared Coronavirus as a Pandemic on 11th March 2020. In this study, our primary aim is to Detect the Severe Covid-19 patient in the Early Stages by looking at the information on admission laboratory values, demographics, comorbidities, admission medications, admission supplementary oxygen orders, discharge, and mortality. 4711 patient's dataset with confirmed SARS-CoV-2 infections are included in the study. Each Patient has total of 85 Features in the Dataset. So, we have Filtered the Top Best 35 features out of 85 features from the Dataset using the seven different feature Selection algorithm and taken the most common features out from the different feature Selection algorithm. After selecting the top most essential features, we have applied around 17 different kinds of ML models like Linear Regression, Logistic regression, SVM, LinearSVC, MLP-Classifier, Decision Tree Classifier, Gradient Boosting Classifier, AdaBoost, Random Forest, XGBoost, LightGBM Classifier, Ridge Classifier, Bagging Classifier, ExtraTreeClassifier, KNN, Naive Bayes, Neural network with Keras, and finally, a Voting Classifier which is the ensemble of all the Top Models from the above-mentioned Models. Finally, all Models are Compared on the basis of Area under the receiver operating characteristic (AUC) get the best AUC as 0.89. © 2021 IEEE.

Financial Risk Prediction Based on Stochastic Block and Cox Proportional Hazards Models

Since 2019, the sudden outbreak of COVID-19 has made huge impacts on various aspects of society, especially the financial industries that are closely related to the national economy and people’s livelihood. Finance is a data-intensive field and its traditional research models include supervised and unsupervised models, state-based models, econometric models, and stochastic models. However, the above models are prone to lose their effectiveness in the situation of an extremely complex financial ecosystem with a large number of nonlinear unpredictable effects, such as those caused by COVID-19. To address this issue, we comprehensively explore and fuse Stochastic Block Model (SBM) and Cox Proportional Hazards Model (COX) for a reliable and accurate financial risk prediction. Specifically, SBM, which is popular in social network analysis, is employed to capture the impact factors on the financial industry in public emergencies, and COX is then leveraged to determine the duration of the impact factors. An extensive experimental evaluation validates the effectiveness of our framework in predicting financial risk. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Behavior-Based Early Cervical Cancer Risk Detection Using Artificial Neural Networks

In a worldwide perspective of the most common cancer diseases, cervical cancer is ranked fourth most frequent whereas the worldwide mortality rate is at 54.56%. In the Philippines, the second leading site among women is cervical cancer next to breast cancer. Research shows that cervical cancer is one of the most treatable cancer forms if detected and managed early. Currently, the most reliable diagnosis and prevention method of cervical cancer is thru a regular testing via Pap Smear test and HPV vaccination being performed in hospitals worldwide. However, according to the Centers for Disease Control and Prevention in California, the cervical cancer screening rate of regular testing in hospitals went down significantly during the stay-at-home order by the government due to the COVID-19 pandemic. Also, there are limited research based on the behavior information in relation to cervical cancer risk prediction, but existing studies proves the possibility of the risk prediction based on behavior information. This paper presents an Artificial Neural Network-based model for early cervical cancer risk detection based on behavior information. The neural network was trained using scaled conjugate gradient back propagation. The system showed 98% overall correctness in early cervical cancer risk prediction. © 2021 IEEE.

Risk Prediction of COVID-19 Positive Patients: How well does the machine learning tools perform?

The pandemic of COVID-19 is spreading everywhere in the world which subsequently has led the world into the most existential health emergency, even in the second wave. Machine learning (ML) has already proved as a promising field to guide the future course of actions in healthcare as a part of combat the pandemic. In this paper, we have applied five algorithms, namely, Random Forest, Decision Tree, Ctree, Naïve Bayes, and PCA have been used to forecast the threatening death risk among the confirmed cases of Covid-19 patients. Since COVID-19 disease is more prevalent in the lungs so we've divided our data into two parts and applied the ML methods on it. Three different predictions have been showed by five of the ML models, where the decision tree for outcome-1, outcome-2 outperforms, and the random forest for outcome-3 performs best than the rest of all. In particular, the results show that which method works best on COVID-19 dataset as well as the prior indication of adverse health factors of the infected patient. Finally, we showed them the alive and death prediction percentage for randomly selected ten patients that demonstrate the capability of ML models. By these sorts of research, we can Figure out whether the affected people have to be taken to ICU or ought to be dealt with at home. Moreover, accuracy performance metric has been determined in two different testing set to identify the most efficient model for risk prediction. © 2021 IEEE.

An Epidemic Spread Risk Prediction Model Coupled with LSTM Algorithm and Cloud Model

The COVID-19 epidemic poses a great threat to public health and people's lives, which has initiated new challenges to the prevention and control system of the epidemic in China. In all efforts for epidemic control and prevention, predicting the risk of epidemic spread is of great practical importance for scientific prevention and control, and precise strategies. To predict the risk of an epidemic rapidly and quantitatively, this paper fused multi-source spatiotemporal data and established a risk prediction model for epidemic transmission by coupling LSTM algorithm and cloud model. Firstly, a simulation model of the spatiotemporal spread of infectious diseases was built based on GIS and LSTM algorithm, which simulated the infectious disease's spatiotemporal transmission process by learning rules in historical epidemic data. At the same time, to improve the simulation accuracy, this paper took 1 km × 1 km for the spatial scale, and days for the temporal scale as the study scale. Secondly, this paper applied the simulated data of infectious cases and the spatiotemporal influence factors on the spread of the epidemic to construct risk evaluation indicators. Finally, the cloud model and adaptive strategies were applied to construct an epidemic risk assessment model. In this way, the epidemic risk assessment at multiple spatial scales was achieved. In the empirical study phase, based on the Beijing COVID-19 epidemic data from 11 June 2020 to 25 June 2020, this paper simulated the process of the spatial evolution of the epidemic from 26 June 2020 to 1 July 2020. To test the advantage of the LSTM model applied to simulate spatiotemporal spread of infectious diseases, four machine learning models were introduced for comparison, including GA-BP Neural Network, Decision Regression Tree, Random Forest, and Support Vector Machine. The results were as follows: ① Compared with other conventional machine learning models, the LSTM model with time-series relationship had higher simulation accuracy (MAE=0.002 61) and better fitting degree (R-Square=0.9455). This showed that the LSTM model considering the temporal relationship between epidemic data was more suitable for epidemic spatial evolution simulation. ② The application results showed that the coupled model can not only fully consider the influence of infection source factors, weather factors, epidemic spread factors and epidemic prevention factors on the spread of transmission risk and reflect the trend of risk evolution, but also quickly quantify regional risk levels. Therefore, the coupled model based on LSTM algorithm and cloud model can effectively predict the transmission risk of epidemic, and also provide a method reference for establishing spatial-temporal transmission models and assessing epidemic risk. 2021, Science Press. All right reserved.

A Method for Dynamic Risk Assessment and Prediction of Public Health Emergencies based on an Improved SEIR Model: Novel Coronavirus COVID-19 in Ten European Countries

Public health emergencies can seriously affect public health and people's lives, and risk assessment and prediction provide a scientific basis for effective prevention and control of public health emergencies. This work proposes a new method for risk dynamic assessment and prediction of public health emergencies based on a revised SEIR model. This work combines transmission rules of public health emergencies with demographic, medical, and economic conditions and establishes rational and comprehensive indices of risk assessment by coupling hazard evaluation and vulnerability estimation. An integrated model of entropy-AHP is employed to implement risk dynamic assessments of public health emergencies. Moreover, this work establishes a modified SEIR model and combines infectious disease transmission dynamics and risk assessment to predict evolutional trends and dynamic risks. The COVID-19 epidemic at the end of December 2019 was an important public health emergency characterized by rapid spread, widespread infection, and great difficulty in prevention and control. The COVID-19 epidemic in 10 European countries is employed as a case study for risk assessment and dynamic prediction. Based on the epidemic data from the beginning to April 16, 2020, the epidemic evolutionary trends and dynamic risks are predicted in these countries from April 17, 2020 to May 10, 2020. According to the prediction results, the epidemic situation in 10 European countries will be severe by May 10, 2020. The goodness of fit R2 is larger than 0.92, and the prediction results are basically consistent with the real epidemic situation. Work resumption will be unfavorable for epidemic prevention and control in this case. The method proposed in this work may offer continuous epidemic risk assessments and predictions for countries and regions with serious outbreaks, support effective decisions for disease prevention and control, and also provide emergency risk evaluations and predictions in new epidemic outbreak periods and for other public security emergencies in the future. 2021, Science Press. All right reserved.