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Shapley Additive Explanation (SHAP) values were used to assess the importance of each predictor. To provide both model-based and statistical perspectives, SHAP analysis was complemented with classical Cox regression, allowing for transparent assessment of each predictor's contribution to the model and its statistical association with survival. Sensitivity analyses incorporating different exposure lags were conducted. The study included 424,725 farm managers (6,265 PD cases) and 544,788 farmworkers (2,848 PD cases) aged 50+, analyzed separately due to differences in available variables and coding structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Several occupational factors, including duration of involvement in crop farming and viticulture, emerged as key promoting predictors, surpassing age in predictive importance. Beyond conventional predictors such as type 2 diabetes, less conventional predictors were identified, including work diversification, seasonal employment, hypercholesterolemia, epilepsy, antidepressant use, anxiolytic use, and antibiotic use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These results contribute to a growing body of evidence supporting the integration of occupational health considerations into PD research and highlight the importance of exploring and identifying potential farming-related risk factors in PD development.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Plain language summary title<\/jats:title>\n                    <jats:p>\n                      <jats:bold>Using nationwide French farming data and machine learning to explore potential factors linked to Parkinson's disease. This study investigates how work-related and health-related factors may contribute to Parkinson's disease risk among farm managers and farmworkers<\/jats:bold>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/1877718x261453798","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T12:36:32Z","timestamp":1785587792000},"page":"1174-1190","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging machine learning with real-world data for hypothesis generation by identifying exposomic predictors in Parkinson's disease among farmers"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9015-5230","authenticated-orcid":false,"given":"Pascal","family":"Petit","sequence":"first","affiliation":[{"name":"Univ. 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