Scholarlink

The classification of diseases as Diabetes, Anemia, Thalassemia, Heart illnesses, Thrombocytopenia, and Health is important for the diagnosis and treatment within the sphere of healthcare. This paper offers a new approach to the categorization of syndromes employing ensemble learning approaches. In ensemble learning procedure, Bagging Trees, the Random Forest Algorithm, and the Extra Trees modules are used to improve the classifier’s speed and ability to avoid noise data. Feature selection techniques are employed in extracting features from different medical data sources and thus enhancing discriminant property of made models. A cost–benefit analysis is therefore commenced, whereby the performance of the combined ensemble models is compared with that of the conventional single-model techniques. Numerous studies that examine medical datasets show that our approach to ensemble learning significantly outperforms other frameworks for the investigation of a range of conditions. This study greatly enriches the knowledge of syndrome classification and provides a reliable adjacent syntactic classifier for clinicians and scholars who are involved in various types of disorders.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Ensemble Learning Techniques for Common Multi-syndrome Classification

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Sheshang Degadwala,
  • Darshanaben Dipakkumar Pandya,
  • Vanita Dandhwani,
  • Nita Jashugiri Goswami,
  • Vandanabahen Gopalbhai Patel,
  • Dhairya Vyas

摘要

The classification of diseases as Diabetes, Anemia, Thalassemia, Heart illnesses, Thrombocytopenia, and Health is important for the diagnosis and treatment within the sphere of healthcare. This paper offers a new approach to the categorization of syndromes employing ensemble learning approaches. In ensemble learning procedure, Bagging Trees, the Random Forest Algorithm, and the Extra Trees modules are used to improve the classifier’s speed and ability to avoid noise data. Feature selection techniques are employed in extracting features from different medical data sources and thus enhancing discriminant property of made models. A cost–benefit analysis is therefore commenced, whereby the performance of the combined ensemble models is compared with that of the conventional single-model techniques. Numerous studies that examine medical datasets show that our approach to ensemble learning significantly outperforms other frameworks for the investigation of a range of conditions. This study greatly enriches the knowledge of syndrome classification and provides a reliable adjacent syntactic classifier for clinicians and scholars who are involved in various types of disorders.