TY - GEN
T1 - Explainable Machine Learning Based Prediction of Severity of Heart Failure Using Primary Electronic Health Records
AU - Ganesan, Rajarajeswari
AU - Habraken, Simon C.
AU - van de Vosse, Frans N.
AU - Huberts, Wouter
PY - 2024/8/22
Y1 - 2024/8/22
N2 - Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from primary Electronic Health Records (EHRs). We used a public dataset of 2008 HF patients for the study. Gaussian Naive Bayes, Random Forest and CatBoost methods were used for prediction. The study shows that CatBoost works best for the goal. In addition to that, the largest contributors for tree-based models harmonize well with clinically important parameters, which exhibits the trustworthiness of these models. Hence, we conclude that utilization of ML methods on primary EHRs is a promising step for time-efficient diagnosis of HF patients.
AB - Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from primary Electronic Health Records (EHRs). We used a public dataset of 2008 HF patients for the study. Gaussian Naive Bayes, Random Forest and CatBoost methods were used for prediction. The study shows that CatBoost works best for the goal. In addition to that, the largest contributors for tree-based models harmonize well with clinically important parameters, which exhibits the trustworthiness of these models. Hence, we conclude that utilization of ML methods on primary EHRs is a promising step for time-efficient diagnosis of HF patients.
KW - Explainable AI
KW - Heart Failure
KW - Machine Learning
KW - Primary Electronic Health Records
KW - Severity of Illness Index
KW - Heart Failure/diagnosis
KW - Diagnosis, Computer-Assisted
KW - Humans
KW - Bayes Theorem
KW - Electronic Health Records
UR - http://www.scopus.com/inward/record.url?scp=85202000176&partnerID=8YFLogxK
U2 - 10.3233/SHTI240471
DO - 10.3233/SHTI240471
M3 - Conference contribution
C2 - 39176799
AN - SCOPUS:85202000176
T3 - Studies in Health Technology and Informatics
SP - 542
EP - 546
BT - Digital Health and Informatics Innovations for Sustainable Health Care Systems
A2 - Mantas, John
A2 - Hasman, Arie
A2 - Demiris, George
A2 - Saranto, Kaija
A2 - Marschollek, Michael
A2 - Arvanitis, Theodoros N.
A2 - Ognjanović, Ivana
A2 - Benis, Arriel
A2 - Gallos, Parisis
A2 - Zoulias, Emmanouil
A2 - Andrikopoulou, Elisavet
PB - IOS Press
T2 - 34th Medical Informatics Europe Conference, MIE 2024
Y2 - 25 August 2024 through 29 August 2024
ER -