Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm

Wonse Kim, Jin Joo Park, Hae Young Lee, Kye Hun Kim, Byung Su Yoo, Seok Min Kang, Sang Hong Baek, Eun Seok Jeon, Jae Joong Kim, Myeong Chan Cho, Shung Chull Chae, Byung Hee Oh, Woong Kook, Dong Ju Choi

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Objective: Machine learning (ML) algorithm can improve risk prediction because ML can select features and segment continuous variables effectively unbiased. We generated a risk score model for mortality with ML algorithms in East-Asian patients with heart failure (HF). Methods: From the Korean Acute Heart Failure (KorAHF) registry, we used the data of 3683 patients with 27 continuous and 44 categorical variables. Grouped Lasso algorithm was used for the feature selection, and a novel continuous variable segmentation algorithm which is based on change-point analysis was developed for effectively segmenting the ranges of the continuous variables. Then, a risk score was assigned to each feature reflecting nonlinear relationship between features and survival times, and an integer score of maximum 100 was calculated for each patient. Results: During 3-year follow-up time, 32.8% patients died. Using grouped Lasso, we identified 15 highly significant independent clinical features. The calculated risk score of each patient ranged between 1 and 71 points with a median of 36 (interquartile range: 27–45). The 3-year survival differed according to the quintiles of the risk score, being 80% and 17% in the 1st and 5th quintile, respectively. In addition, ML risk score had higher AUCs than MAGGIC-HF score to predict 1-year mortality (0.751 vs. 0.711, P < 0.001). Conclusions: In East-Asian patients with HF, a novel risk score model based on ML and the new continuous variable segmentation algorithm performs better for mortality prediction than conventional prediction models. Clinical Trial Registration: Unique identifier: INCT01389843

Original languageEnglish
Pages (from-to)1321-1333
Number of pages13
JournalClinical Research in Cardiology
Issue number8
Publication statusPublished - 2021 Aug

Bibliographical note

Funding Information:
This work was supported by Research of Korea Centers for Disease Control and Prevention [2010-E63003-00, 2011-E63002-00, 2012-E63005-00, 2013-E63003-00, 2013-E63003-01, 2013-E63003-02, and 2016-ER6303-00].

Publisher Copyright:
© 2021, Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine


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