HCDP-DELM: Heterogeneous chronic disease prediction with temporal perspective enabled deep extreme learning machine

Abstract

The strain on healthcare systems is growing dramatically due to the significant rise in heterogeneous chronic disease incidence. Even though frequent patient monitoring is complex and expensive, analyzing chronic illness transitions and predicting patients at risk of developing ailments is crucial to healthcare. This paper proposes an innovative prediction framework for heterogeneous chronic disease prediction (HCDP), including hypertension (HT), heart disease (HD), and chronic kidney disease (CKD), and develops a patient symptom-weighted network (PSWN) by projecting the bipartite graph. Hence, PSWN represents the relationship between health conditions for patients with similar illnesses. LSTM was used to capture the temporal relationship between features in the patient network using a complex network theory. Finally, a deep extreme learning machine (DELM) was applied as a classifier to predict heterogeneous chronic disease. Subsequently, the proposed model was evaluated against previous studies, and the result revealed that the proposed model was more accurate. Remarkably, the proposed architecture achieves an accuracy of 95.02% for hypertension, 96.15% for heart disease, and 98.39% for chronic kidney disease prediction. The relevance of the model is also evaluated with the use of the Wilcoxon signed-rank test. As evidenced in this study, the suggested model outperformed previous research and state-of-the-art approaches, suggesting its potential for a pivotal role in the medical decision-making of heterogeneous chronic diagnoses. Furthermore, the HCDP-DELM model contributes to healthcare authorities by establishing a system of smart surveillance and precise forecasting that allows for delayed disease detection and provides insightful potential strategies for developing effective healthcare.