Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.

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Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

Remote Sensing ◽

10.3390/rs13051021 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1021

Author(s):

Hu Ding ◽

Jiaming Na ◽

Shangjing Jiang ◽

Jie Zhu ◽

Kai Liu ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Loess Plateau ◽

Water Conservation ◽

Nearest Neighbor ◽

Gradient Boosting ◽

K Nearest Neighbor ◽

The Loess Plateau ◽

Object Based ◽

Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

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Bacterial Immunogenicity Prediction by Machine Learning Methods

Vaccines ◽

10.3390/vaccines8040709 ◽

2020 ◽

Vol 8 (4) ◽

pp. 709

Author(s):

Ivan Dimitrov ◽

Nevena Zaharieva ◽

Irini Doytchinova

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Predictive Ability ◽

Initial Step ◽

Majority Voting ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Test Set ◽

Extreme Gradient Boosting

The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis, k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), random subspace method (RSM), and extreme gradient boosting) on a set of 317 known bacterial immunogens and 317 bacterial non-immunogens and derived models for immunogenicity prediction. The models were validated by internal cross-validation in 10 groups from the training set and by the external test set. All of them showed good predictive ability, but the xgboost model displays the most prominent ability to identify immunogens by recognizing 84% of the known immunogens in the test set. The combined RSM-kNN model was the best in the recognition of non-immunogens, identifying 92% of them in the test set. The three best performing ML models (xgboost, RSM-kNN, and RF) were implemented in the new version of the server VaxiJen, and the prediction of bacterial immunogens is now based on majority voting.

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Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach

Journal of Advanced Transportation ◽

10.1155/2021/7763126 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Xu Bao ◽

Yanqiu Li ◽

Jianmin Li ◽

Rui Shi ◽

Xin Ding

Keyword(s):

Nearest Neighbor ◽

Pso Algorithm ◽

Bayesian Optimization ◽

Gradient Boosting ◽

K Nearest Neighbor ◽

Delay Management ◽

Buffer Time ◽

Proposed Model ◽

Learning Machine ◽

Hidden Layer

In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. Next, an ELM with one hidden layer is developed to predict train arrival delays by considering these characteristics mentioned before as input features. Furthermore, the PSO algorithm is chosen to optimize the hyperparameter of the ELM compared to Bayesian optimization and genetic algorithm solving the arduousness problem of manual regulating. Finally, a case is studied to confirm the advantage of the proposed model. Contrasted to four baseline models (k-nearest neighbor, categorical boosting, Lasso, and gradient boosting decision tree) across different metrics, the proposed model is demonstrated to be proficient and achieve the highest prediction accuracy. In addition, through a detailed analysis of the prediction error, it is found that our model possesses good robustness and correctness.

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Application of Artificial Intelligence and Machine Learning Techniques in Classifying Extent of Dementia Across Alzheimer's Image Data

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2021040103 ◽

2021 ◽

Vol 6 (2) ◽

pp. 29-46

Author(s):

Robin Ghosh ◽

Anirudh Reddy Cingreddy ◽

Venkata Melapu ◽

Sravanthi Joginipelli ◽

Supratik Kar

Keyword(s):

Neural Network ◽

Machine Learning ◽

Nearest Neighbor ◽

Image Data ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Mild Dementia ◽

Extreme Gradient Boosting

Alzheimer's disease (AD) is one of the most common forms of dementia and the sixth-leading cause of death in older adults. The presented study has illustrated the applications of deep learning (DL) and associated methods, which could have a broader impact on identifying dementia stages and may guide therapy in the future for multiclass image detection. The studied datasets contain around 6,400 magnetic resonance imaging (MRI) images, each segregated into the severity of Alzheimer's classes: mild dementia, very mild dementia, non-dementia, moderate dementia. These four image specifications were used to classify the dementia stages in each patient applying the convolutional neural network (CNN) algorithm. Employing the CNN-based in silico model, the authors successfully classified and predicted the different AD stages and got around 97.19% accuracy. Again, machine learning (ML) techniques like extreme gradient boosting (XGB), support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN) offered accuracy of 96.62%, 96.56%, 94.62, and 89.88%, respectively.

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IgA Nephropathy Prediction in Children with Machine Learning Algorithms

Future Internet ◽

10.3390/fi12120230 ◽

2020 ◽

Vol 12 (12) ◽

pp. 230

Author(s):

Ping Zhang ◽

Rongqin Wang ◽

Nianfeng Shi

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Learning Algorithms ◽

Immunoglobulin A ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Chi Square ◽

Extreme Gradient Boosting

Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of renal failure. IgAN prediction in children with machine learning algorithms has been rarely studied. We retrospectively analyzed the electronic medical records from the Nanjing Eastern War Zone Hospital, chose eXtreme Gradient Boosting (XGBoost), random forest (RF), CatBoost, support vector machines (SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM) models in order to predict the probability that the patient would not reach or reach end-stage renal disease (ESRD) within five years, used the chi-square test to select the most relevant 16 features as the input of the model, and designed a decision-making system (DMS) of IgAN prediction in children that is based on XGBoost and Django framework. The receiver operating characteristic (ROC) curve was used in order to evaluate the performance of the models and XGBoost had the best performance by comparison. The AUC value, accuracy, precision, recall, and f1-score of XGBoost were 85.11%, 78.60%, 75.96%, 76.70%, and 76.33%, respectively. The XGBoost model is useful for physicians and pediatric patients in providing predictions regarding IgAN. As an advantage, a DMS can be designed based on the XGBoost model to assist a physician to effectively treat IgAN in children for preventing deterioration.

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A hybrid evolutionary learning classification for robot ground pattern recognition

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202940 ◽

2021 ◽

pp. 1-15

Author(s):

Jiankai Zuo ◽

Yaying Zhang

Keyword(s):

Nearest Neighbor ◽

Fitness Function ◽

Gradient Boosting ◽

Support Vector ◽

Evolutionary Learning ◽

Ensemble Classifiers ◽

Improved Genetic Algorithm ◽

K Nearest Neighbor ◽

Obvious Effect ◽

Extreme Gradient Boosting

In the field of intelligent robot engineering, whether it is humanoid, bionic or vehicle robots, the driving forms of standing, moving and walking, and the consciousness discrimination of the environment in which they are located have always been the focus and difficulty of research. Based on such problems, Naive Bayes Classifier (NBC), Support Vector Machine(SVM), k-Nearest-Neighbor (KNN), Decision Tree (DT), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were introduced to conduct experiments. The six individual classifiers have an obvious effect on a particular type of ground, but the overall performance is poor. Therefore, the paper proposes a “Novel Hybrid Evolutionary Learning” method (NHEL) which combines every single classifier by means of weighted voting and adopts an improved genetic algorithm (GA) to obtain the optimal weight. According to the fitness function and evolution times, this paper designs the adaptively changing crossover and mutation rate and applies the conjugate gradient (CG) to enhance GA. By making full use of the global search capabilities of GA and the fast local search ability of CG, the convergence speed is accelerated and the search precision is upgraded. The experimental results show that the performance of the proposed model is significantly better than individual machine learning and ensemble classifiers.

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Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson’s Disease Detection

Diagnostics ◽

10.3390/diagnostics11061076 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1076

Author(s):

Muntasir Hoq ◽

Mohammed Nazim Uddin ◽

Seung-Bo Park

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Nearest Neighbor ◽

Neurodegenerative Disorder ◽

Imbalanced Data ◽

Principal Component ◽

Gradient Boosting ◽

Support Vector ◽

K Nearest Neighbor ◽

Extreme Gradient Boosting

As a neurodegenerative disorder, Parkinson’s disease (PD) affects the nerve cells of the human brain. Early detection and treatment can help to relieve the symptoms of PD. Recent PD studies have extracted the features from vocal disorders as a harbinger for PD detection, as patients face vocal changes and impairments at the early stages of PD. In this study, two hybrid models based on a Support Vector Machine (SVM) integrating with a Principal Component Analysis (PCA) and a Sparse Autoencoder (SAE) are proposed to detect PD patients based on their vocal features. The first model extracted and reduced the principal components of vocal features based on the explained variance of each feature using PCA. For the first time, the second model used a novel Deep Neural Network (DNN) of an SAE, consisting of multiple hidden layers with L1 regularization to compress the vocal features into lower-dimensional latent space. In both models, reduced features were fed into the SVM as inputs, which performed classification by learning hyperplanes, along with projecting the data into a higher dimension. An F1-score, a Mathews Correlation Coefficient (MCC), and a Precision-Recall curve were used, along with accuracy to evaluate the proposed models due to highly imbalanced data. With its highest accuracy of 0.935, F1-score of 0.951, and MCC value of 0.788, the probing results show that the proposed model of the SAE-SVM surpassed not only the former model of the PCA-SVM and other standard models including Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Random Forest (RF), but also surpassed two recent studies using the same dataset. Oversampling and balancing the dataset with SMOTE boosted the performance of the models.

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Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽

10.3390/en14051377 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1377

Author(s):

Musaab I. Magzoub ◽

Raj Kiran ◽

Saeed Salehi ◽

Ibnelwaleed A. Hussein ◽

Mustafa S. Nasser

Keyword(s):

Machine Learning ◽

Rheological Properties ◽

Nearest Neighbor ◽

Drilling Fluid ◽

Gradient Boosting ◽

K Nearest Neighbor ◽

Wide Range ◽

Machine Learning Approach ◽

Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

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mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

Bioinformatics ◽

10.1093/bioinformatics/bty1047 ◽

2018 ◽

Vol 35 (16) ◽

pp. 2757-2765 ◽

Cited By ~ 63

Author(s):

Balachandran Manavalan ◽

Shaherin Basith ◽

Tae Hwan Shin ◽

Leyi Wei ◽

Gwang Lee

Keyword(s):

Nearest Neighbor ◽

Feature Representation ◽

Superior Performance ◽

Supplementary Information ◽

Gradient Boosting ◽

Support Vector ◽

Pharmaceutical Drugs ◽

K Nearest Neighbor ◽

Feature Descriptors ◽

Predicted Probability

AbstractMotivationCardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction.ResultsIn this study, we utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF) and support vector machine (SVM) using 51 feature descriptors derived from eight different feature encodings for the prediction of AHTPs. While ERT-based trained models performed consistently better than other algorithms regardless of various feature descriptors, we treated them as baseline predictors, whose predicted probability of AHTPs was further used as input features separately for four different ML-algorithms (ERT, GB, RF and SVM) and developed their corresponding meta-predictors using a two-step feature selection protocol. Subsequently, the integration of four meta-predictors through an ensemble learning approach improved the balanced prediction performance and model robustness on the independent dataset. Upon comparison with existing methods, mAHTPred showed superior performance with an overall improvement of approximately 6–7% in both benchmarking and independent datasets.Availability and implementationThe user-friendly online prediction tool, mAHTPred is freely accessible at http://thegleelab.org/mAHTPred.Supplementary informationSupplementary data are available at Bioinformatics online.

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Modeling and analysis of COVID-19 new deaths using tree-based ensemble

10.36227/techrxiv.16566012.v1 ◽

2021 ◽

Author(s):

Ibrahim Abaker Targio Hashem ◽

Raja Sher Afgun Usmani ◽

Asad Ali Shah ◽

Abdulwahab Ali Almazroi ◽

Muhammad Bilal

Keyword(s):

Infectious Disease ◽

United States ◽

Random Forest ◽

Economic Activity ◽

The United States ◽

Gradient Boosting ◽

Health Crisis ◽

Modeling And Analysis ◽

Extreme Gradient Boosting ◽

The World

The COVID-19 pandemic has emerged as the world's most serious health crisis, affecting millions of people all over the world. The majority of nations have imposed nationwide curfews and reduced economic activity to combat the spread of this infectious disease. Governments are monitoring the situation and making critical decisions based on the daily number of new cases and deaths reported. Therefore, this study aims to predict the daily new deaths using four tree-based ensemble models i.e., Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Voting Regressor (VR) for the three most affected countries, which are the United States, Brazil, and India. The results showed that VR outperformed other models in predicting daily new deaths for all three countries. The predictions of daily new deaths made using VR for Brazil and India are very close to the actual new deaths, whereas the prediction of daily new deaths for the United States still needs to be improved.<br>

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