Handling missing values in data mining - A case study of heart failure dataset

In this paper, we investigate the characteristics of a clinical dataset using feature selection and classification techniques to deal with missing values and develop a method to quantify numerous complexities. The research aims to find features that have high effect on mortality time frame, and to design methodologies which will cope with the following challenges: missing values, high dimensionality, and the prediction problem. The experimental results will be extended to develop prediction model for HF This paper also provides a comprehensive evaluation of a set of diverse machine learning schemes for clinical datasets.