Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran
ABSTRACT Background and Aims Infertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule‐mining methods,...
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Wiley
2025-01-01
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| Series: | Health Science Reports |
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| Online Access: | https://doi.org/10.1002/hsr2.70265 |
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| author | Hosna Heydarian Masoumeh Abbasi Farid Najafi Mitra Darbandi |
| author_facet | Hosna Heydarian Masoumeh Abbasi Farid Najafi Mitra Darbandi |
| author_sort | Hosna Heydarian |
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| description | ABSTRACT Background and Aims Infertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule‐mining methods, to analyze diverse patient data and uncover relevant insights. This approach involves a thorough analysis of patients' clinical characteristics, dietary habits, and overall conditions to identify complex patterns and relationships that may contribute to infertility. Methods In this study, we examined the impact of lifestyle factors on infertility using machine learning and data mining techniques, specifically Association Rules. The study included a total of 4437 women who participated in the Ravansar Non‐Communicable Disease Cohort study. Among the remaining participants, 434 were infertile. We utilized 38 variables to generate the relevant association rules. Results As a result, the analysis reveals that 97% of infertile women are likely to cook for more than 2 h and engage in standing activities. Additionally, 94% of infertile women are likely to have central obesity. Infertile women also have a 73% chance of reusing cooking oil and a 74% chance of consuming fried food at least once a week. The likelihood of infertility increases to 98% among women who use more than 24 eggs per month and to 97% among those who consume moldy jam or syrup. The evaluation of these associations was further supported by measures of support, confidence, and lift. Conclusion This study showed that key lifestyle factors linked to infertility, underscoring the role of lifestyle in reproductive health. These findings suggest that targeted interventions and lifestyle changes may help reduce infertility rates. Further research is needed to confirm these associations and investigate the underlying mechanisms. |
| format | Article |
| id | doaj-art-169249a598334a15b13fc2bb39779609 |
| institution | Kabale University |
| issn | 2398-8835 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
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| series | Health Science Reports |
| spelling | doaj-art-169249a598334a15b13fc2bb397796092025-01-29T03:42:39ZengWileyHealth Science Reports2398-88352025-01-0181n/an/a10.1002/hsr2.70265Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in IranHosna Heydarian0Masoumeh Abbasi1Farid Najafi2Mitra Darbandi3Department of Information Technology Engineering, Industrial and Systems Engineering Faculty Tarbiat Modares University Tehran IranDepartment of Health Information Technology, School of Allied Medical Sciences Kermanshah University of Medical Science Kermanshah IranResearch Center for Environmental Determinants of Health (RCEDH), Health Institute Kermanshah University of Medical Sciences Kermanshah IranResearch Center for Environmental Determinants of Health (RCEDH), Health Institute Kermanshah University of Medical Sciences Kermanshah IranABSTRACT Background and Aims Infertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule‐mining methods, to analyze diverse patient data and uncover relevant insights. This approach involves a thorough analysis of patients' clinical characteristics, dietary habits, and overall conditions to identify complex patterns and relationships that may contribute to infertility. Methods In this study, we examined the impact of lifestyle factors on infertility using machine learning and data mining techniques, specifically Association Rules. The study included a total of 4437 women who participated in the Ravansar Non‐Communicable Disease Cohort study. Among the remaining participants, 434 were infertile. We utilized 38 variables to generate the relevant association rules. Results As a result, the analysis reveals that 97% of infertile women are likely to cook for more than 2 h and engage in standing activities. Additionally, 94% of infertile women are likely to have central obesity. Infertile women also have a 73% chance of reusing cooking oil and a 74% chance of consuming fried food at least once a week. The likelihood of infertility increases to 98% among women who use more than 24 eggs per month and to 97% among those who consume moldy jam or syrup. The evaluation of these associations was further supported by measures of support, confidence, and lift. Conclusion This study showed that key lifestyle factors linked to infertility, underscoring the role of lifestyle in reproductive health. These findings suggest that targeted interventions and lifestyle changes may help reduce infertility rates. Further research is needed to confirm these associations and investigate the underlying mechanisms.https://doi.org/10.1002/hsr2.70265association rulesclinical decision makingdata mininginfertilityPERSIAN cohort |
| spellingShingle | Hosna Heydarian Masoumeh Abbasi Farid Najafi Mitra Darbandi Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran Health Science Reports association rules clinical decision making data mining infertility PERSIAN cohort |
| title | Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran |
| title_full | Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran |
| title_fullStr | Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran |
| title_full_unstemmed | Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran |
| title_short | Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran |
| title_sort | data mining of infertility and factors influencing its development a finding from a prospective cohort study of rancd in iran |
| topic | association rules clinical decision making data mining infertility PERSIAN cohort |
| url | https://doi.org/10.1002/hsr2.70265 |
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