機械学習による退職調整の予測における身体的健康と認知能力の要因:中国健康と退職長期研究の結果
Chenhong Cui1, Bolin Zhou2, Lin Gao3
1Faculty of Psychology, Tianjin Normal University, Tianjin, China.
Frontiers in psychology
|September 5, 2025
まとめ
退職への適応の成功は 自己評価された聴覚,認知能力,健康といった要素に依存します 機械学習モデルは 記憶と睡眠の非線形効果を含む 重要な予測要因を特定し よりよい退職計画を立てました
科学分野:
- ゲロントロジー
- 認知科学
- 健康 経済
背景:
- 退職は人生の大きな変化で 福祉に影響を与えます
- 身体的・認知的な健康は 退職後の成功に不可欠です
- 多様な健康と認知の指標とその非線形的な影響の同時調査は欠けている.
研究 の 目的:
- 機械学習を用いた退職調整における 物理的・認知的変数の予測的役割を調査する.
- 退職時の調整に成功するための重要な予測要因を特定する.
- 健康/認知要因と年金調整の非線形関連を調べる
主な方法:
- 中国保健・退職研究 (CHARLS) の縦断データを使用した.
- 退職調整を予測するためにRandom Forest (RF) とXGBoostの分類モデルを使用した.
- モデル予測を解釈し,影響する変数を特定するためにSHAP値分析を適用した.
主要な成果:
- RFとXGBoostモデルは,正規化されたロジスティック回帰よりも高い精度を示した.
- 主な予測要因は 自己評価の聴覚,収入,注意力,自己評価の健康,時間指向でした
- 自己評価された記憶と睡眠時間は 退職時の調整と非線形的な関係を示した.
結論:
- 機械学習は 退職前の要因に基づいて 退職時の調整を効果的に予測します
- 特定の身体的健康と 認知能力は 移行の成功に不可欠です
- 退職後の調整と福祉の改善のための介入の洞察を提供します.
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