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Selecting the appropriate, reasonable, and affordable health insurance plan becomes a very important question to solve for both employers and employees. Our research tries to locate the factors determining private sector health insurance plan enrolment decision, and also provides a guideline to both private companies and employees on health insurance plan selection strategies. By using Kaiser Family Foundation Annual Employer Health Benefits Survey (KFF EHBS) data, we apply random decision forest machine learning methodology to study the determinants of employees' health insurance selection, as well as to compare the prediction accuracy among different methodologies. The results indicate: 1) the employees at large firms and the firms with higher eligible rate would tend to choose PPO plan; 2) employees who need family coverage would have different choices comparing employees who seek for single coverage only; 3) employer's contribution and annual total contribution to the health insurance plan are the most important determinants on employees' insurance selection. The conclusion also can provide some suggestions to insurance companies on health insurance package design for different types of employers and employees.

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