A Novel Classification Method for Syndrome Differentiation of Patients with AIDS.

Yufeng Zhao,Lin Luo,Guozheng Li,Qi Xie,Jian Wang,Baoyan Liu,Xiaoping Zhang,Kun Li,Xiang Zhang,Xianghong Jing,Liyun He

doi:10.1155/2015/936290

Abstract

We consider the analysis of an AIDS dataset where each patient is characterized by a list of symptoms and is labeled with one or more TCM syndromes. The task is to build a classifier that maps symptoms to TCM syndromes. We use the minimum reference set-based multiple instance learning (MRS-MIL) method. The method identifies a list of representative symptoms for each syndrome and builds a Gaussian mixture model based on them. The models for all syndromes are then used for classification via Bayes rule. By relying on a subset of key symptoms for classification, MRS-MIL can produce reliable and high quality classification rules even on datasets with small sample size. On the AIDS dataset, it achieves average precision and recall 0.7736 and 0.7111, respectively. Those are superior to results achieved by alternative methods.

Highlights

Acquired immune deficiency syndrome (AIDS) is common and extremely harmful to humans
The following criteria are used to evaluate the performance of AIDS syndrome differentiation based on minimum reference setbased multiple instance learning (MRS-multiple instance learning (MIL))
minimum reference set (MRS)-MIL-based classification methods facilitated the building of syndrome differentiation models for patients with AIDS

Summary

Introduction

Acquired immune deficiency syndrome (AIDS) is common and extremely harmful to humans. Significant clinical practice [3,4,5,6,7,8,9,10,11,12,13,14] proved that TCM is better at improving clinical symptoms and quality of life in patients with human immunodeficiency virus (HIV) infection/AIDS clinical symptoms. In TCM, treatment based on syndrome differentiation is the basis of clinical assessment and clinical study. Since TCM usually describes diseases with qualitative and fuzzy quantitative words, there is no clear functional relationship between the symptoms and syndromes [15]. For AIDS, there are few systematic studies of quantitative syndrome differentiation because of the disease complexity and novelty [4]. Exploring the objective and inherent relationships between the symptoms and syndromes, followed by constructing classification models of syndromes, is a fast developing field

Methods

Results

Discussion

Conclusion