{'fraction_non_alphanumeric': 0.010378057820607857, 'fraction_numerical': 0.0, 'mean_word_length': 5.081081081081081, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Home-based Early Risk Detection System for Congestive Heart Failure using a Bayesian Reasoning Network

Congestive heart failure (CHF) is a progressive condition in which the heart is no longer capable of supplying adequate oxygenated blood to the body. Since the incidence of CHF increases with age, mainly due to the development of heart failure risk factors the epidemic of CHF is expected to grow further in the coming decades and thus becoming an important public health problem. In this paper we present a risk detection system for CHF that uses a Bayesian Network (BN) combined with health measurements that can be taken in a home environment using ambient assisted living technologies. The algorithm is empowered by employing statistical and medical analysis of the stored biological data and the output can be used as a basis for triggering proper preventive interventions. The BN design was established by surveying the relevant literature and consulting the domain expert. The network content combines both biometric variables that are daily monitored and data from patient’s clinical history as well as additional heart failure risk factors in terms of the EuroSCORE model. The predictive validity was tested with the involvement of the domain expert who specified proper validation rules in terms of criteria for detecting a CHF risk.