# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 4 Experiments  
### Data Set and Experiment Setup  
Patients with at least one of the following seven diseases--diabetes mellitus type 2, hyperlipemia, hyperuricemia, coronary illness, cerebral ischemic stroke, anemia, and chronic kidney disease--were viewed in a local hospital named Haikou People's Hospital. Then, 655 patients satisfying the above diseases were selected as experimental examples. After selecting features from their essential information and laboratory results, five essential characteristics and 278 items of laboratory test results were combined to construct the features of experimental examples. The essential characteristics included age, temperature, height, weight, and gender (the detailed testing items are illustrated in the Appendix A). Binary value was used to represent the estimation of gender, i.e., male was 0 and female was 1. The values of age, temperature, height, and weight were kept as their actual numerical qualities. The corresponding values of testing items were divided into three groups: normal (the corresponding value is in the normal range); low (the corresponding value is lower than the minimum value in the normal range); and high (the corresponding value is higher than the maximum value in the normal range). Furthermore, the values of testing items recorded by textual information were classified into these groups with the suggestion of a medical expert. The corresponding values of items were set as normal if the patient had not checked these items. The measurements of the final data and those of the final labels are outlined in Tables 2 and 3. (The detailed list of testing items is shown in Table 11). In the experimental examples, 42.6% were female and 57.4% were male. The mean age, temperature, height, and weight of experimental examples were 62.72, 36.6, 168.35, and 65.47, respectively. The values of features were standardized using the data standardization method introduced in Section 3.1 before the training process. In addition, principal component analysis (PCA) was performed for dimensionality reduction in the feature preprocess. \begin{table}
\begin{tabular}{l} \hline
**Algorithm: COCO-RE** \\ \hline
**Outputs:** \\ \(Y\): the suggested labels for \(x\) \\
**Tianing process:** \\
1: For \(j=1\) to \(q\)do \\
2: Generate the binary training set \(D_{j}\) according to Equation (3) \\
3: \(g_{j}\Leftcurlyeq B(D_{j})\); \\
4: Select a subset \(L_{k}\subseteq Y\backslash g_{j}\) containing \(K\) labels randomly \\
5: for \(y_{k}\Leftcurlyeq L_{k}\)do \\
6: Generate the tri-class training set \(D_{jk}^{tri}\) according to Equation (5) \\
7: Initialize example weight \(w_{0}(i)=1/M\) and \(\delta_{1}=1\) \\
8: for \(t=1\) to \(T\)do \\
9: Train a classifier \(f_{t}\Left\operatorname*{argmin}\limits_{i\in D_{j}}w_{t-1}(i)\mathbf{1}[y_{i }\
eq f_{t}(x_{i})]\) \\
10: if\(t>1\)then \\
11: Compute \(\delta_{t}\) according to Equation (13) \\
12: end if \\
13: if\(\varepsilon_{t}>1/(1+\delta_{t}^{-1}(C-1)^{-1})\)then \\
14: return \(\alpha_{t}\Leftcurlyeq 0\) \\
15: else \\
16: Compute weight \(\alpha_{t}\) for classifier \(f_{t}\): \(\alpha_{t}\Leftcurlyeq\frac{1-\alpha_{t}}{\alpha_{t}}+\frac{1}{2}\log(\delta_ {t}(C-1))\) \\
17: Compute example weight: \(w_{t}(i)\Leftw_{t-1}(i)e^{i\alpha_{t}[i,y_{i}\
eq f_{t}(x_{i})]}\) \\
18: Normalize \(w_{t}(i)\): \(w_{t}(i)\Leftw_{t-1}(i)\) \\
19: end if \\
20: end for \\
21: \(g_{A}\Leftcurlyeq\operatorname*{arg\,max}\limits_{y}\sum\limits_{t=1}^{T} \alpha_{t}f_{t}(x)\) \\
22: end for \\
23: Set the real-valued function \(f_{j}(\cdot)\): \(f_{j}(x)\Leftcurlyeq g_{j}(+1|x)+\sum_{y_{B}\in L_{k}}\delta_{A}(+2\big{|}x)\) \\
24: Set the constant thresholding function \(t_{j}(\cdot)\) is equal to \(\alpha_{j}\) generated by Equation (7) \\
25: end for \\
26: Return \(Y=h(x)\) according to Equation (2) \\ \hline \end{tabular}
\end{table} 
Table 1: Cont. The results of the COCO-RE approach were compared against two series of multi-label learning methods towards class-imbalanced data.