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In-Depth Information
We summarized research results that: 1. The decision-tree algorithm for fatty liver
screening has an accuracy of 86.2%, and it is better than logistic regression; 2. The
accuracy of decision-tree algorithm for moderate to severe fatty liver disease is
93%; 3. The cut points of six parameters in decision-tree algorithm are: 1. Layoff,
Age=40~50, Sex=M, Mar=Y, Shi=Normal shift, Pos=Manager_Lv and
Com
50000 had an approximately 86.2% accuracy rate for predicting the
laid-off employees. 2. Layoff, Age=40~50, Sex=M, Mar=Y, Edu=University,
Pos=Manager_Lv and Com
≧
50000 had an approximately 92.17% accuracy rate
for predicting the laid-off employees. 3. Layoff, Age=40~50, Sex=M, Mar=Y,
Gra
≧
50000 had an approximately 96.44%
accuracy rate for predicting the laid-off employees. In consequence, we find the
high compensation, high position, high grade, high education level to become the
dangerous list of layoff.
≧
10 , Pos=Manager_Lv and Com
≧
Fig. 12
Decision Tree analysis
4 Discussion and Conclusion
This chapter provided a new research direction of combing SNA and DM methods
in HRM. We examine structural positions of individuals, especially HR actors
(line managers and employees) within relational networks for building layoff pre-
diction model and to explore implications for designing and implementing HR
practices.
This study aims to verify the main causes for layoff factors. This research in-
tends to base on these factors and concepts that addressed by above to find a best
layoff predictive model using the social network and data mining techniques.
Through an empirical evaluation, the results indicated that the proposed approach
has pretty good prediction accuracy by using organizational networks relationship,
employees databases, and layoff records to build layoff prediction model. Both
