A comparative study of missing value imputation with multiclass classification for clinical heart failure data

Zhang, Y.; Kambhampati, C.; Davis, D. N.; Goode, K.; Cleland, J. G. F.

doi:10.1109/fskd.2012.6233805

All Outputs (4)

Disease progression in chronic heart failure is linear: Insights from multistate modelling (2024)
Journal Article
Kazmi, S., Kambhampati, C., Rigby, A. S., Cleland, J. G. F., Kazmi, K. S., Cuthbert, J., Pellicori, P., & Clark, A. L. (online). Disease progression in chronic heart failure is linear: Insights from multistate modelling. European journal of heart failure, https://doi.org/10.1002/ejhf.3400

Aims: Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. Methods and results:... Read More about Disease progression in chronic heart failure is linear: Insights from multistate modelling.

Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure (2022)
Journal Article
Kazmi, S., Kambhampati, C., Cleland, J., Cuthbert, J., Kazmi, K. S., Pellicori, P., …Clark, A. L. (2022). Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure. ESC Heart Failure, https://doi.org/10.1002/ehf2.14028

Aims: Risk changes with the progression of disease and the impact of treatment. We developed a dynamic risk stratification Markov chain model using artificial intelligence in patients with chronic heart failure (CHF). Methods and results: We describe... Read More about Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure.

Use of a propositional rule learner for prognosis of mortality rates in heart failure patients (2014)
Journal Article
Bohacik, J., Kambhampati, C., Davis, D. N., & Cleland, J. G. F. (2014). Use of a propositional rule learner for prognosis of mortality rates in heart failure patients. Journal of information technologies, 7(1),

A comparative study of missing value imputation with multiclass classification for clinical heart failure data (2012)
Presentation / Conference Contribution
Zhang, Y., Kambhampati, C., Davis, D. N., Goode, K., & Cleland, J. G. F. A comparative study of missing value imputation with multiclass classification for clinical heart failure data. Presented at 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

Clinical data often contains missing values. Imputation is one of the best known schemes to overcome the drawbacks associated with missing values in data mining tasks. In this work, we compared several imputation methods and analyzed their performanc... Read More about A comparative study of missing value imputation with multiclass classification for clinical heart failure data.