Categorical BoostingとBeesアルゴリズムを用いた心血管疾患の早期検出のための説明可能なハイブリッドフレームワーク
Jayanta Sen1, Sweta Bhattacharya2
1School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India. jayanta.sen@vit.ac.in.
Scientific reports
|December 13, 2025
まとめ
新しいハイブリッド機械学習(ML)モデルは、心血管疾患(CVD)を正確に検出し、標的療法の解釈可能な結果を提供します。この高度なフレームワークは、早期疾患検出と治療計画を向上させます。
科学分野:
- 心血管の健康
- 機械学習の応用
- 医療における人工知能
背景:
- 心血管疾患(CVD)は、世界的な死亡原因の主な原因です。
- 早期のCVD検出は、効果的な患者管理のために非常に重要です。
- 疾患予測のための従来の機械学習(ML)モデルは、多くの場合、透明性を欠いています(「ブラックボックス」です)。
主な方法:
- Framingham心血管疾患(CVD)データセットを利用しました。
- データバランスのためにランダムオーバーサンプリング(RO)を適用し、正規化のためにMin-Maxスケーリングを適用しました。
- カテゴリカルブースティング(CatBoost)とBEEsアルゴリズムを組み合わせたハイブリッドMLモデルを開発しました。
- 解釈性のために説明可能な人工知能(XAI)技術(LIME、SHAP)を実装しました。
結論:
- 提案されたハイブリッドMLモデルは、CVD検出のための非常に正確で解釈可能なソリューションを提供します。
- 説明可能なAIは、医療提供者に貴重な洞察を提供し、タイムリーで正確な治療決定を促進します。
- このフレームワークは、心血管疾患の管理と患者の転帰を大幅に改善する可能性があります。
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