Purpose:Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance.Additionally,the interpretabilit...Purpose:Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance.Additionally,the interpretability of these models presents a persistent challenge.Design/methodology/approach:This paper proposes two innovative dimensionality reduction models based on integer programming(DRMBIP).These models assess compactness through the correlation of each indicator with its class center,while separation is evaluated by the correlation between different class centers.In contrast to DRMBIP-p,the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation.Findings:This study,getting data from the Global Health Observatory(GHO),investigates 141 indicators that influence life expectancy.The findings reveal that DRMBIP-p effectively reduces the dimensionality of data,ensuring compactness.It also maintains compatibility with other models.Additionally,DRMBIP-v finds the optimal result,showing exceptional separation.Visualization of the results reveals that all classes have a high compactness.Research limitations:The DRMBIP-p requires the input of the correlation threshold parameter,which plays a pivotal role in the effectiveness of the final dimensionality reduction results.In the DRMBIP-v,modifying the threshold parameter to variable potentially emphasizes either separation or compactness.This necessitates an artificial adjustment to the overflow component within the objective function.Practical implications:The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators.Validated by life expectancy data,this paper demonstrates potential to assist data miners with the reduction of data dimensions.Originality/value:To our knowledge,this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering.It not only has applications in life expectancy,but also has obvious advantages in data mining work that requires precise class centers.展开更多
Background: Occupation is a significant factor in life, health and well-being. Long-term military service is a unique career path that may influence life expectancy, even after excluding obvious risks such as battlefi...Background: Occupation is a significant factor in life, health and well-being. Long-term military service is a unique career path that may influence life expectancy, even after excluding obvious risks such as battlefield mortality. However, it remains unclear what the effects of a military career are on the life trajectory of personnel who retire from service. We aimed to compare life expectancy among retired military personnel(RMP) to their sex- and birth cohortspecific reference populations.Methods: For this historical-cohort study we collected data on sex, year of birth, year of death, time in service, and rank at end of service for 4,862 Israeli RMPs. Data on reference populations were provided by the Israel Central Bureau of Statistics, by birth decade from 1900 to 1989. We calculated the difference between each individual RMP's age at death and the "expected" age at death, based on sex- and birth cohort-specific means in the reference populations. Results: Overall, 67.9% of RMPs lived longer than average relative to their sex specific birth cohort. This difference in life expectancy was more pronounced among women than among men. There was a significant trend of increasing difference between RMP males and reference males over time(P<0.002), whereas no significant trend was identified among females. Length of service and rank were not associated with relative longevity for RMPs.Conclusions: The mechanism of the protective effect of military service on life expectancy remains unknown, but our findings indicate that it affects men and women differently, with women being more likely to benefit from the protective effect of military service. The healthy worker effect is known to vary from one occupation to another. To the best of our knowledge, this is the first attempt to quantify the magnitude of the healthy worker effect among career military servicemen and women.展开更多
基金supported by the National Natural Science Foundation of China (Nos.72371115)the Natural Science Foundation of Jilin,China (No.20230101184JC)。
文摘Purpose:Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance.Additionally,the interpretability of these models presents a persistent challenge.Design/methodology/approach:This paper proposes two innovative dimensionality reduction models based on integer programming(DRMBIP).These models assess compactness through the correlation of each indicator with its class center,while separation is evaluated by the correlation between different class centers.In contrast to DRMBIP-p,the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation.Findings:This study,getting data from the Global Health Observatory(GHO),investigates 141 indicators that influence life expectancy.The findings reveal that DRMBIP-p effectively reduces the dimensionality of data,ensuring compactness.It also maintains compatibility with other models.Additionally,DRMBIP-v finds the optimal result,showing exceptional separation.Visualization of the results reveals that all classes have a high compactness.Research limitations:The DRMBIP-p requires the input of the correlation threshold parameter,which plays a pivotal role in the effectiveness of the final dimensionality reduction results.In the DRMBIP-v,modifying the threshold parameter to variable potentially emphasizes either separation or compactness.This necessitates an artificial adjustment to the overflow component within the objective function.Practical implications:The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators.Validated by life expectancy data,this paper demonstrates potential to assist data miners with the reduction of data dimensions.Originality/value:To our knowledge,this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering.It not only has applications in life expectancy,but also has obvious advantages in data mining work that requires precise class centers.
文摘Background: Occupation is a significant factor in life, health and well-being. Long-term military service is a unique career path that may influence life expectancy, even after excluding obvious risks such as battlefield mortality. However, it remains unclear what the effects of a military career are on the life trajectory of personnel who retire from service. We aimed to compare life expectancy among retired military personnel(RMP) to their sex- and birth cohortspecific reference populations.Methods: For this historical-cohort study we collected data on sex, year of birth, year of death, time in service, and rank at end of service for 4,862 Israeli RMPs. Data on reference populations were provided by the Israel Central Bureau of Statistics, by birth decade from 1900 to 1989. We calculated the difference between each individual RMP's age at death and the "expected" age at death, based on sex- and birth cohort-specific means in the reference populations. Results: Overall, 67.9% of RMPs lived longer than average relative to their sex specific birth cohort. This difference in life expectancy was more pronounced among women than among men. There was a significant trend of increasing difference between RMP males and reference males over time(P<0.002), whereas no significant trend was identified among females. Length of service and rank were not associated with relative longevity for RMPs.Conclusions: The mechanism of the protective effect of military service on life expectancy remains unknown, but our findings indicate that it affects men and women differently, with women being more likely to benefit from the protective effect of military service. The healthy worker effect is known to vary from one occupation to another. To the best of our knowledge, this is the first attempt to quantify the magnitude of the healthy worker effect among career military servicemen and women.
