Data Mining Techniques has attained its momentum in several areas, and its efficient performance in decision support has outperformed and made it a reliable choice. The medical world is one such empirical domain in which a perfect decision at right time would turn out to be a lifesaver. Medical data figures out to be majorly multi-dimensional, where relevant feature extraction is a challenging factor. Several classification approaches like SVM, Decision Trees, and Naive Based are considered to handle these profound challenges. One such challenge discussed in our paper emphasizing on Medical decision support system with Machine Learning (ML) Methodology considering diseases and treatments with their semantic relations in the document of Pub med abstracts. The proposed Multi variant classification framework aims at reducing data into attributes using PCA Transformation infusion with an efficient classification Algorithm - CNB. Our computed results are comparatively successful in attaining ultimate outcomes concerning performance metrics like Accuracy, Precision, Recall, and Time. The strength of our work lies in presenting an efficient approach for elevating enhanced decisions in Health care.

中文翻译：

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一种短文本医学数据多变量分类的新方法

Data Mining Techniques 在多个领域获得了发展势头，其在决策支持方面的高效性能表现优异，成为可靠的选择。医学界就是这样一个经验领域，在该领域中，在正确的时间做出完美的决定将成为救命稻草。医学数据主要是多维的，其中相关特征提取是一个具有挑战性的因素。SVM、决策树和基于朴素的几种分类方法被认为可以应对这些深刻的挑战。我们的论文中讨论了一个这样的挑战，该论文强调使用机器学习 (ML) 方法论的医学决策支持系统考虑疾病和治疗及其在 Pub med 摘要文档中的语义关系。提议的多变体分类框架旨在使用 PCA 转换注入和有效的分类算法 - CNB 将数据减少为属性。我们的计算结果在获得有关准确度、精确度、召回率和时间等性能指标的最终结果方面相对成功。我们工作的优势在于提出了一种有效的方法来提升医疗保健领域的决策。