Objective To develop a method for nou-invasive risk model that can be used in health management system and primary healthcare centers. Methods Based on systematic phenotype measurement and modified superviser-based machine learning, an applicable and low-cost method for non-invasive disease risk prediction model was developed. Invasive risk assessment model for Chinese isehemic cardiovascular diseases (ICVD) was used as a supervised standard reference to provide evidence based non-invasive model with skin resistance on 24 acupeints. Results A total of 4606 cases from a physical examination system were collected to develop the model. For those reasonable evaluation indeies, the coincidence of non-invasive with invasive risk prediction model was > 90% on average level. Conclusions This method could be used to develop non-invasive risk assessment model for the common diseases.