基于boosting叠加集成方法的甲状腺疾病预测模型

Subhash Mondal, Souptik Dutta, Soumadip Ghosh, Sarbartha Gupta, Dhrubajit Kakati, A. Nag
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引用次数: 0

摘要

甲状腺在人体的新陈代谢、生长发育中起着重要的作用。虽然它不是一种危及生命的疾病,但患有甲状腺的人在日常生活中会面临许多并发症。最近的趋势表明,女性比男性更容易患甲状腺相关疾病。许多导致甲状腺疾病的因素可以在早期诊断阶段得到控制。机器学习预测模型可以帮助医疗保健专业人员在初始阶段诊断甲状腺疾病并采取相应措施。本研究在9172个具有相关特征的实例的数据集上部署了最初的16个ML模型,包括6个增强算法。通过各种标准性能指标来判断模型的性能。其中Cat Boost (CB)模型的准确率最高,达到95.75%。通过实现随机搜索CV对提升模型进行超参数调整,将CB的准确率提高到96.19%。以CB分类器为元学习器,在6个增强调优模型上应用叠加集成方法。同时,保留其他增强算法作为最终模型预测的基础学习器。与默认模型相比,堆栈模型的准确性令人印象深刻,达到95.32%,ROC-AUC为0.95,其他结果也很有希望。模型的标准差显著小于0.57,说明模型具有较好的稳定性和稳健性,假阴性(False Negative, FN)率达到1.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thyroid Disease Prediction Model on Boosting-based Stacking Ensemble Approach
The thyroid gland plays a significant role in the human body's metabolism, growth, and development. Though it is not a life-threatening disease, a person suffering from thyroid faces many complications in their daily life. Recent trends have shown that women suffer more from thyroid-related diseases than men. The many contributing factors that lead to thyroid disease may be controlled upon early diagnosis stages. Machine learning prediction models help healthcare professionals diagnose thyroid diseases at an initial stage and take measures accordingly. This study deployed initial Sixteen ML models, including six boosting algorithms, on a dataset of 9172 instances with related features. The model performances have been judged through various standard performance metrics. The boosting algorithms showed exceptional results, and Cat Boost (CB) model produced the best accuracy of 95.75%. The hyperparameter tuning performed on boosting models by implementing Randomized Search CV increased the accuracy to 96.19% for CB. The stacking ensemble approach was applied on top of the six boosting tuned models with the CB classifier as the meta-learner. At the same time, the other boosting algorithms were kept as a base learner for the final model prediction. The accuracy of the stack model was impressive, with 95.32% compared with default models, the ROC-AUC at 0.95, and the other results were also promising. The model’s standard deviation was significantly less at 0.57, implying the model’s stability and robustness, and the False Negative (FN) rate reached 1.8%.
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