The Prediction Model for Classification of COVID-19 Infected Patients Using Vital Sign

Icing forecasting for transmission lines is of great significance for anti-icing strategies in power grids, but existing prediction models have some disadvantages such as application limitations, weak generalization, and lack of global prediction ability. To overcome these shortcomings, this paper suggests a new conception about a segmental icing prediction model for transmission lines in which the classification of icing process plays a crucial role. In order to obtain the classification, a hierarchical K-means clustering method is utilized and 11 characteristic parameters are proposed. Based on this method, 97 icing processes derived from the Icing Monitoring System in China Southern Power Grid are clustered into six categories according to their curve shape and the abstracted icing evolution curves are drawn based on the clustering centroid. Results show that the processes of ice events are probably different and the icing process can be considered as a combination of several segments and nodes, which reinforce the suggested conception of the segmental icing prediction model. Based on monitoring data and clustering, the obtained types of icing evolution are more comprehensive and specific, and the work lays the foundation for the model construction and contributes to other fields.

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Constructing and Evaluating a Churn Prediction Model using Classification of User Types in MMORPG

KIISE Transactions on Computing Practices ◽

10.5626/ktcp.2018.24.5.220 ◽

2018 ◽

Vol 24 (5) ◽

pp. 220-226

Author(s):

Sejoon Oh ◽

Eunjo Lee ◽

Jiyoung Woo ◽

Huy Kang Kim

Keyword(s):

Prediction Model ◽

Churn Prediction

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Visual Classification of Music Style Transfer Based on PSO-BP Rating Prediction Model

Complexity ◽

10.1155/2021/9959082 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Tianjiao Li

Keyword(s):

Prediction Model ◽

Real Time ◽

Nonlinear Problems ◽

Style Transfer ◽

Collaborative Filtering Algorithm ◽

Item Attribute ◽

Pixel Color ◽

Rating Prediction ◽

Time Changes

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm.

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A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study (Preprint)

10.2196/preprints.15965 ◽

2019 ◽

Author(s):

Wongeun Song ◽

Se Young Jung ◽

Hyunyoung Baek ◽

Chang Won Choi ◽

Young Hwa Jung ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intensive Care ◽

High Resolution ◽

Prediction Model ◽

Neonatal Sepsis ◽

Vital Sign ◽

Prediction Models ◽

Late Onset ◽

Model Based

BACKGROUND Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. OBJECTIVE The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. METHODS We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. RESULTS The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. CONCLUSIONS The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

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Letter: evaluation and proposed re‐classification of HCC prediction model in patients with chronic hepatitis C genotype 4

Alimentary Pharmacology & Therapeutics ◽

10.1111/apt.16704 ◽

2021 ◽

Vol 55 (2) ◽

pp. 255-257

Author(s):

Gamal Shiha ◽

Nabiel N. H. Mikhail ◽

Reham Soliman

Keyword(s):

Chronic Hepatitis ◽

Hepatitis C ◽

Prediction Model ◽

Chronic Hepatitis C ◽

Genotype 4

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Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices With Efficient Machine Learning Technique

10.21203/rs.3.rs-1183824/v1 ◽

2021 ◽

Author(s):

Jingjing Xia ◽

Jin Zeng

Keyword(s):

Machine Learning ◽

Water Quality ◽

Water Resources ◽

Environmental Factors ◽

Prediction Model ◽

Water Quality Index ◽

Quality Index ◽

Efficient Machine ◽

Method Of Correlation

Abstract Water is an indispensable resource for human production and life. The evaluation of water quality by scientific method that provides sufficient support for the regeneration and recycling utilization of water resources. At present, water quality is mainly evaluated by water quality index (WQI) with weighted entropy value, which comprehensively considers the influence of different relevant environmental factors on the water quality. The calculation process is very complicated and time-consuming. In this paper, the method of correlation analysis is used to select the best combination of relevant environmental factors to assist the prediction model. Two typical kinds of machine learning methods are adopted and compared to realize the prediction of entropy water quality index (EWQI). After the better framework of prediction model is selected, four different kinds of optimization algorithms are used to optimize the prediction model to realize non-linear regression prediction and classification of water quality. According to the results of evaluation indicators, the framework of SVM is more suitable for realizing the prediction of EWQI. Meanwhile, the optimization algorithm of DE-GWO show great potential to improve the performance of SVM, which can make further contribution to the rational use and protection of water resources.

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Stock Market Prediction Performance of Neural Networks: A Literature Review

International Journal of Economics and Finance ◽

10.5539/ijef.v9n11p100 ◽

2017 ◽

Vol 9 (11) ◽

pp. 100 ◽

Cited By ~ 9

Author(s):

Özgür Ican ◽

Taha Bugra Çelik

Keyword(s):

Neural Networks ◽

Prediction Model ◽

Mean Squared Error ◽

Forecast Error ◽

Absolute Error ◽

Absolute Deviation ◽

Error Measures ◽

Squared Error ◽

Number Of Publications

In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

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A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study

JMIR Medical Informatics ◽

10.2196/15965 ◽

2020 ◽

Vol 8 (7) ◽

pp. e15965

Author(s):

Wongeun Song ◽

Se Young Jung ◽

Hyunyoung Baek ◽

Chang Won Choi ◽

Young Hwa Jung ◽

...

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Intensive Care ◽

High Resolution ◽

Prediction Model ◽

Neonatal Sepsis ◽

Vital Sign ◽

Prediction Models ◽

Late Onset ◽

Model Based

Background Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. Objective The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. Methods We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. Results The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. Conclusions The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

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