BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of surv...BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.展开更多
As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely ...As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely contributes to the literature by employing the planetary pressures-adjusted human development index(PHDI)as an indicator of GHD,which integrates environmental impacts into human development.Using static and dynamic econometric methods,including the quantile regression and autoregressive distributed lag model,it explores the impacts of renewable and nonrenewable energy consumption on GHD.The findings demonstrate that renewable energy currently has a detrimental impact on GHD due to its limited adoption and high costs.Conversely,nonrenewable energy positively influences GHD,as it is the primary energy source in the country and is becoming more efficient at reducing emissions.However,the study finds that greater use of renewable energy reduces its adverse effects,suggesting that as renewable energy technologies become more cost-effective and widely implemented,their initial adverse effects could be mitigated,leading to improved long-term GHD outcomes.These findings carry important implications for Indonesia,where the govern‐ment is striving to expand renewable energy capacity while promoting equitable development across its archi‐pelagic regions.They underscore the critical role of energy policy in balancing economic,social,and environmental goals,contributing meaningfully to the country’s sustainable development agenda.展开更多
基金The authors sincerely thank the Clinical Outcomes Research and Education at Collegeof Dental Medicine, Roseman University of Health Sciences for supporting this study.
文摘BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.
文摘As the world’s fourth most populous country,Indonesia presents challenges and opportunities for sustainable energy progress,offering a critical context to investigate green human development(GHD).This study uniquely contributes to the literature by employing the planetary pressures-adjusted human development index(PHDI)as an indicator of GHD,which integrates environmental impacts into human development.Using static and dynamic econometric methods,including the quantile regression and autoregressive distributed lag model,it explores the impacts of renewable and nonrenewable energy consumption on GHD.The findings demonstrate that renewable energy currently has a detrimental impact on GHD due to its limited adoption and high costs.Conversely,nonrenewable energy positively influences GHD,as it is the primary energy source in the country and is becoming more efficient at reducing emissions.However,the study finds that greater use of renewable energy reduces its adverse effects,suggesting that as renewable energy technologies become more cost-effective and widely implemented,their initial adverse effects could be mitigated,leading to improved long-term GHD outcomes.These findings carry important implications for Indonesia,where the govern‐ment is striving to expand renewable energy capacity while promoting equitable development across its archi‐pelagic regions.They underscore the critical role of energy policy in balancing economic,social,and environmental goals,contributing meaningfully to the country’s sustainable development agenda.
