Health Checkup Data Analysis Focusing on Body Mass Index
This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age,...
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| Veröffentlicht in: | IEICE Transactions on Information and Systems 2017/08/01, Vol.E100.D(8), pp.1634-1641 |
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| container_title | IEICE Transactions on Information and Systems |
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| creator | HIGUCHI, Mizuki SORACHI, Kenichi HATA, Yutaka |
| description | This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on gender-age, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. We show a possibility that we could prevent the lifestyle disease by controlling BMI. |
| doi_str_mv | 10.1587/transinf.2016LOP0009 |
| format | Article |
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We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on gender-age, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. 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Inf. & Syst.</addtitle><description>This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on gender-age, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. We show a possibility that we could prevent the lifestyle disease by controlling BMI.</description><subject>Age</subject><subject>Body Mass Index</subject><subject>Body size</subject><subject>Classification</subject><subject>Data analysis</subject><subject>Disease control</subject><subject>Gender</subject><subject>Inspection</subject><subject>lifestyle-related diseases</subject><subject>Mann-Whitney U test</subject><subject>specific health checkup</subject><subject>Wilcoxon Rank-Sum test</subject><issn>0916-8532</issn><issn>1745-1361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNpNkMFOwkAQhjdGExF9Aw-beC7Odjvb7RFBhASDMXreTNstFLHF3TaRt7cGQU4zh__7M_MxditgIFDH942jypdVMQhBqPniBQCSM9YTcYSBkEqcsx4kQgUaZXjJrrxfAwgdCuwxPbW0aVZ8tLLZR7vlY2qIDyva7Hzp-aTO2q54yeuKP9T5jj-T93xW5fb7ml0UtPH25m_22fvk8W00DeaLp9loOA8yVHETYK4oLVKLlFoZFxZyihCkQImgMY0VRaGVoCxqiDC1hc0QE0pkSKRDlLLP7va9W1d_tdY3Zl23rjvQm-7bCDHSKulS0T6Vudp7ZwuzdeUnuZ0RYH4dmYMjc-Kow1732No3tLRHiFxTZhv7Dz0KADM2-rCclBzD2YqcsZX8AXHbeBY</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>HIGUCHI, Mizuki</creator><creator>SORACHI, Kenichi</creator><creator>HATA, Yutaka</creator><general>The Institute of Electronics, Information and Communication Engineers</general><general>Japan Science and Technology Agency</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20170101</creationdate><title>Health Checkup Data Analysis Focusing on Body Mass Index</title><author>HIGUCHI, Mizuki ; SORACHI, Kenichi ; HATA, Yutaka</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c567t-5d6abfbe5abe37fe0da45031535085b76a42e306e58045befec559a932aa82533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Age</topic><topic>Body Mass Index</topic><topic>Body size</topic><topic>Classification</topic><topic>Data analysis</topic><topic>Disease control</topic><topic>Gender</topic><topic>Inspection</topic><topic>lifestyle-related diseases</topic><topic>Mann-Whitney U test</topic><topic>specific health checkup</topic><topic>Wilcoxon Rank-Sum test</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>HIGUCHI, Mizuki</creatorcontrib><creatorcontrib>SORACHI, Kenichi</creatorcontrib><creatorcontrib>HATA, Yutaka</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEICE Transactions on Information and Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>HIGUCHI, Mizuki</au><au>SORACHI, Kenichi</au><au>HATA, Yutaka</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Health Checkup Data Analysis Focusing on Body Mass Index</atitle><jtitle>IEICE Transactions on Information and Systems</jtitle><addtitle>IEICE Trans. Inf. & Syst.</addtitle><date>2017-01-01</date><risdate>2017</risdate><volume>E100.D</volume><issue>8</issue><spage>1634</spage><epage>1641</epage><pages>1634-1641</pages><issn>0916-8532</issn><eissn>1745-1361</eissn><abstract>This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on gender-age, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. We show a possibility that we could prevent the lifestyle disease by controlling BMI.</abstract><cop>Tokyo</cop><pub>The Institute of Electronics, Information and Communication Engineers</pub><doi>10.1587/transinf.2016LOP0009</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | J-STAGE Free; EZB-FREE-00999 freely available EZB journals |
| subjects | Age Body Mass Index Body size Classification Data analysis Disease control Gender Inspection lifestyle-related diseases Mann-Whitney U test specific health checkup Wilcoxon Rank-Sum test |
| title | Health Checkup Data Analysis Focusing on Body Mass Index |
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