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スマートフォンの過剰依存を予測し,機械学習に基づく高齢者の影響要因を分析する
Haiyan Kong1,2, Xinyu Wang3, Hualong Fang4
1School of Business, Xinyang Normal University, Xinyang, China.
Frontiers in psychology
|February 12, 2026
まとめ
この研究では,機械学習を使用した高齢者のスマートフォン依存症の主要なリスク要因を特定しました. XGBoostは過度依存を正確に予測し,人口統計と使用パターンを重要な要因として強調しました.
科学分野:
- ゲロントロジーはゲロントロジーの学科です.
- デジタル・ヘルス・デジタル・ヘルス
- コンピュータサイエンス コンピュータサイエンス
背景:
- 高齢者の間でスマートフォンの使用が増加することは,過剰な使用と依存のリスクを伴います.
- スマートフォンの過剰依存は,中年および高齢者の間でますます懸念される問題です.
- これらのリスクを理解することは,標的を絞った介入の開発に不可欠です.
研究 の 目的:
- 韓国における高齢者のスマートフォン依存症のリスク要因を特定し予測する.
- スマートフォンの過剰依存を予測するための機械学習モデルを開発・評価する.
- 高齢者におけるスマートフォンの過剰依存を緩和するための戦略を策定する.
主な方法:
- 2023 スマートフォン依存度調査 (韓国) のパネルデータを活用した.
- 60歳以上の成人に焦点を当て,機械学習バイナリ分類器 (XGBoost,SVM,LR,KNN,DT,NB) を採用しています.
- 混同マトリックス,精度,精度,リコール,F1スコア,AUCを使用してモデルのパフォーマンスを評価します.
主要な成果:
- XGBoostは,スマートフォンの過剰依存に対する最も高い予測精度 (0.925) を実証しました.
- 特定された主要な予測要因には,人口統計的特徴,スマートフォンを使用する時間,過剰依存意識,および使用内容が含まれています.
- 機能の重要性分析は,スマートフォンの過剰依存に対する重要な影響要因を明らかにしました.
結論:
- 機械学習アルゴリズムは,高齢者のスマートフォンの過剰依存のリスクを効果的に予測し,説明することができます.
- 特定されたリスク要因は,予防措置の開発に貴重な洞察を提供します.
- この発見は,高齢者向けのデジタルヘルスの介入について,理論的・実践的な意味合いを持つ.
キーワード:
デジタル行動 デジタル行動機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) と呼ばれるものです.高齢者,高齢者,高齢者,高齢者リスクファクター リスクファクター リスクファクタースマートフォンの過剰依存 スマートフォンの過剰依存さらに関連する動画
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