使用机器学习解锁用于诊断慢性病的精准医学
Yogita Dubey1, Pranav Mange1, Yash Barapatre1
1Department of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur 441110, India.
Diagnostics (Basel, Switzerland)
|October 14, 2023
概括
机器学习模型有效地检测和预测慢性病 (CKD). 血清肌素,血压和年龄等关键预测因素可以改善早期诊断和患者的结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 慢性病 (CKD) 给全球健康带来了巨大的负担,需要先进的诊断和预后工具.
- 早期检测和准确的预后对于有效的CKD管理和改善患者结果至关重要.
- 机器学习 (ML) 提供了一个有前途的途径,以提高CKD的识别和预测.
研究的目的:
- 探索各种ML算法的有效性,以检测和预测CKD.
- 改善早期CKD检测和预后,从而改善患者的治疗结果,减少医疗保健系统的压力.
- 确定在CKD预测中最具影响力的关键临床和人口特征.
主要方法:
- 使用各种ML算法:梯度提升 (GB),决策树 (DT),K-最近邻居 (KNN),随机森林 (RF),直方图提升 (HB) 和XGBoost (XGB).
- 使用准确性,精度,回忆和F1分数等指标评估算法性能.
- 进行特征意义分析,以精确确定CKD检测和预后的关键预测因子.
主要成果:
- ML算法在检测和预测CKD方面表现出有效性.
- 血清肌水平,血压和年龄被确定为跨模型的持续显著预测因素.
- 功能分析证实了这些属性在早期CKD检测和预后中的关键作用.
结论:
- 多种ML算法是用于CKD检测和预测的有效工具.
- 血清肌素,血压和年龄是早期CKD识别和预后的关键指标.
- 利用ML提高了诊断准确性和效率,从而改善了患者的治疗结果和医疗保健的有效性.
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