Modeling methodology for early warning of chronic heart failure based on real medical big data

Heart failure (HF) is among the most costly diseases to our society, and the prevalence keeps on increasing these days. Early detection of HF plays a vital role in saving lives through adjusting lifestyles and drug interventions that can slow down disease progression or prevent HF. There are many cardiovascular risk factors associated with HF, and they often coexist. In this paper, we assess the predictive value of pathological factors for early HF detection through a social network based approach. We use electronic health records (collected from the project HeartCarer) and compute the similarity of risk factors. The similarity values are used to construct an unweighted and a weighted medical social network. The constructed medical social network is further divided into a HF high-risk group and HF low-risk group using a group division algorithm. Patients in the high-risk group will be suggested for early screening. To evaluate the prediction value of our method, we perform four experiments based on real world data. The results demonstrate the high effectiveness of our method on heart failure risk assessment, with the best accuracy close to 90%.