# High-Risk HPV Cervical Lesion Potential Correlations Mining over Large-Scale Knowledge Graphs
## 6 Lesion Prediction Algorithm Base on Mapping  
There are many missing values in case data, and traditional missing value processing method is not suitable for case data, because any filling method with missing values may cause deviation in prediction results. Therefore, this paper proposes a disease prediction algorithm based on mapping principle, which maps the risk factor keywords extracted from EMR data to key risk factor nodes in the graph model structure. To obtain different relationship values, and in using the different relationship values for lesion likelihood prediction. The idea of lesion prediction base on mapping (LPM) algorithm is shown in Figure 5.  
As shown in Figure 5a, {v2,v6,v8,v12} denote the set of keywords extracted from one patient case data, they exist in the risk factor relationship graph network structure, As shown in Figure 5b, {v9,v10,v11,v12} denote all the key risk factors we get, the keywords in the set of keywords extracted from patient case data may exist in the set of key risk factors or beyond it, therefore, we design the LPM algorithm to map the set of keywords extracted from each patient case data to the key risk factors uniformly, which can get different case information features.  
Our proposed LPM algorithm pseudo-code is shown in Algorithm 2.  
Figure 5: Lesion prediction process example: V2,V6,V8, and V12 are the basic risk factors from patient EMR in (**a**), and V9,V10,V11,and V12 are the newly extracted and confirm the key risk factors based on potential relationship mining, as shown in (**b**).  
\(h_{x}\) is any one keyword in the set of case keywords H, \(x\in(1,u)\), \(f_{y}\) is any one in \(F\), \(y\in(1,v)\), \(Z_{(hx,fy)}\) is the value of the relationship between \(h_{x}\) and \(f_{y}\).  
When \(h_{x}=f_{y}\),\(Z_{(hx,fy)}\) is the maximum value of the edges connected to all neighboring nodes of \(f_{y}\) in the graph model \(G\). When \(h_{x}\
eq f_{y}\), and there is a directly connected edge between \(h_{x}\) and \(f_{y}\) in the graph model \(G\), \(Z_{(hx,f_{y})}=Z_{(hx,fy)}\). When \(h_{x}\
eq f_{y}\), and there is no directly connected edge between \(h_{x}\) and \(f_{y}\) in the graph model \(G\), but there is a path, define the concatenated product of the strength values of the relations passing on the shortest path as \(mul\), and the path length as \(npath\). \(Z_{(hx,fy)}\) was calculated using Equation (4). When \(h_{x}\
eq f_{y}\), and there is no directly connected edge between \(h_{x}\) and \(f_{y}\) in the graph model G, but also not exsit the path, \(Z_{(hx,f_{y})}=0\).  
\[Z_{(h_{x},f_{y})}=\frac{mul}{npath} \tag{4}\]  
the statistical number \(num_{y}\) of relations between the set of case keywords \(H\) and \(f_{y}\):  
\[num_{y}=\sum\limits_{x=1}^{u}1\ Z_{(h_{x},f_{y})}\
eq 0 \tag{5}\]  
The mapping value of the case keyword set \(H\) to the key risk factors:  
\[map_{y}=\frac{\sum\limits_{x=1}^{u}Z_{(h_{x},f_{y})}}{num_{y}} \tag{6}\]The mapping matrix \(MAP=(map_{1},map_{2},map_{3},...,map_{v})^{T}\) of cases is obtained by calculating the mapping values of all key risk factors. defined \(a^{T}\). \(b\) is parameter values in the \(sigmoid\) function obtained by logistic regression model training.  
\(MAP\) is used as the variable value of the case, and the patient risk level \(flevel\) is calculated using the \(sigmoid(MAP,\alpha^{T},b)\). \(flevel>=0.6\), patient with high risk level and high possibility of lesion. \(0.6>flevel>=0.4\), patient with medium risk level and medium possibility of lesion. \(flevel<0.4\), patient with low risk level and low possibility of lesion.