A Comparative Study on Machine Learning Models for Paprika Growth Prediction Model with Temperature Changes

2021 ◽  
Vol 46 (12) ◽  
pp. 2393-2402
Author(s):  
SaravanaKumar Venkatesan ◽  
Jonghyun Lim ◽  
Chanagsun Shin ◽  
Yongyun Cho
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Prediction Model ◽  
Learning Models ◽  
Growth Prediction ◽  
Temperature Changes ◽  
Machine Learning Models

scholarly journals A Comparative Study for Efficient Covid-19 Detecting Machine Learning Models on CT Images

2021 ◽  
Vol 1099 (1) ◽  
pp. 012056
Author(s):  
Ankur Chaturvedi ◽  
Divyansh Mishra ◽  
Dr. Vikram Rajpoot ◽  
Janvi Gupta ◽  
Aditi Sharma
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Ct Images ◽  
Learning Models ◽  
Machine Learning Models

Validating the Early Prediction Model for COPD Patients Care through a Federated Machine Learning Architecture on FAIR Data (Preprint)

2021 ◽  
Author(s):  
Celia ALVAREZ-ROMERO ◽  
Alicia MARTÍNEZ-GARCÍA ◽  
Jara Eloisa TERNERO-VEGA ◽  
Pablo DÍAZ-JIMÉNEZ ◽  
Carlos JIMÉNEZ-DE-JUAN ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Prediction Model ◽  
Prospective Study ◽  
Health Research ◽  
Early Prediction ◽  
Learning Models ◽  
Copd Patients ◽  
Readmission Risk ◽  
Machine Learning Models

BACKGROUND Due to the nature of health data, its sharing and reuse for research are limited by legal, technical and ethical implications. In this sense, to address that challenge, and facilitate and promote the discovery of scientific knowledge, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles help organizations to share research data in a secure, appropriate and useful way for other researchers. OBJECTIVE The objective of this study was the FAIRification of health research existing datasets and applying a federated machine learning architecture on top of the FAIRified datasets of different health research performing organizations. The whole FAIR4Health solution was validated through the assessment of the generated model for real-time prediction of 30-days readmission risk in patients with Chronic Obstructive Pulmonary Disease (COPD). METHODS The application of the FAIR principles in health research datasets in three different health care settings enabled a retrospective multicenter study for the generation of federated machine learning models, aiming to develop the early prediction model for 30-days readmission risk in COPD patients. This prediction model was implemented upon the FAIR4Health platform and, finally, an observational prospective study with 30-days follow-up was carried out in two health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective parts of the study. RESULTS The prediction model for the 30-days hospital readmission risk was trained using the retrospective data of 4.944 COPD patients. The assessment of the prediction model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients in total for the observational prospective study from April 2021 to September 2021. The significant accuracy (0.98) and precision (0.25) of the prediction model generated upon the FAIR4Health platform was observed and, as a result, the generated prediction of 30-day readmission risk was confirmed in 87% of the cases. CONCLUSIONS A clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified datasets from different health research performing organizations, providing an assessment for predicting 30-days readmission risk in COPD patients. This demonstration allowed to state the relevance and need of implementing a FAIR data policy to facilitate data sharing and reuse in health research.

scholarly journals Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study

2019 ◽  
Vol 188 ◽  
pp. 106282 ◽  
Author(s):  
Christos Gkerekos ◽  
Iraklis Lazakis ◽  
Gerasimos Theotokatos
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Fuel Oil ◽  
Engine Fuel ◽  
Learning Models ◽  
Oil Consumption ◽  
Main Engine ◽  
Machine Learning Models

scholarly journals Development of Combined Heavy Rain Damage Prediction Models with Machine Learning

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2516 ◽  
Author(s):  
Changhyun Choi ◽  
Jeonghwan Kim ◽  
Jungwook Kim ◽  
Hung Soo Kim
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Predictive Performance ◽  
Heavy Rain ◽  
Learning Models ◽  
Damage Prediction ◽  
Natural Disaster Management ◽  
Machine Learning Models

Adequate forecasting and preparation for heavy rain can minimize life and property damage. Some studies have been conducted on the heavy rain damage prediction model (HDPM), however, most of their models are limited to the linear regression model that simply explains the linear relation between rainfall data and damage. This study develops the combined heavy rain damage prediction model (CHDPM) where the residual prediction model (RPM) is added to the HDPM. The predictive performance of the CHDPM is analyzed to be 4–14% higher than that of HDPM. Through this, we confirmed that the predictive performance of the model is improved by combining the RPM of the machine learning models to complement the linearity of the HDPM. The results of this study can be used as basic data beneficial for natural disaster management.

scholarly journals Machine Learning and Prediction of All-Cause Mortality among Chinese Older Adults

2021 ◽  
Author(s):  
Xurui Jin ◽  
Yiyang Sun ◽  
Tinglong Zhu ◽  
Yu Leng ◽  
Shuyi Guan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Regression Model ◽  
Predictive Value ◽  
Cox Regression ◽  
Brier Score ◽  
Baseline Survey ◽  
Learning Models ◽  
All Cause Mortality ◽  
Machine Learning Models

AbstractBackground and aimMortality risk stratification was vital for targeted intervention. This study aimed at building the prediction model of all-cause mortality among Chinese dwelling elderly with different methods including regression models and machine learning models and to compare the performance of machine learning models with regression model on predicting mortality. Additionally, this study also aimed at ranking the predictors of mortality within different models and comparing the predictive value of different groups of predictors using the model with best performance.MethodI used data from the sub-study of Chinese Longitudinal Healthy Longevity Survey (CLHLS) - Healthy Ageing and Biomarkers Cohort Study (HABCS). The baseline survey of HABCS was conducted in 2008 and covered similar domains that CLHLS has investigated and shared the sampling strategy. The follow-up of HABCS was conducted every 2-3 years till 2018.The analysis sample included 2,448 participants from HABCS. I used totally 117 predictors to build the prediction model for survival using the HABCS cohort, including 61 questionnaire, 41 biomarker and 15 genetics predictors. Four models were built (XG-Boost, random survival forest [RSF], Cox regression with all variables and Cox-backward). We used C-index and integrated Brier score (Brier score for the two years’ mortality prediction model) to evaluate the performance of those models.ResultsThe XG-Boost model and RSF model shows slightly better predictive performance than Cox models and Cox-backward models based on the C-index and integrated Brier score in predicting surviving. Age. Activity of daily living and Mini-Mental State Examination score were identified as the top 3 predictors in the XG-Boost and RSF models. Biomarker and questionnaire predictors have a similar predictive value, while genetic predictors have no addictive predictive value when combined with questionnaire or biomarker predictors.ConclusionIn this work, it is shown that machine learning techniques can be a useful tool for both prediction and its performance sightly outperformed the regression model in predicting survival.

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