FCMPSO: An Imputation for Missing Data Features in Heart Disease Classification

International Research and Innovation Summit 2017

Salleh, Mohd Najib Mohd^1 ; Samat, Nurul Ashikin^1

Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Batu Pahat

86400, Malaysia^1

关键词: Classification algorithm;    Classification results;    Heart disease;    Hidden knowledge;    Medical areas;    Missing data;    Missing values;    Pre-processing stages;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/226/1/012102/pdf
DOI  :  10.1088/1757-899X/226/1/012102

来源: IOP

【 摘 要 】

The application of data mining and machine learning in directing clinical research into possible hidden knowledge is becoming greatly influential in medical areas. Heart Disease is a killer disease around the world, and early prevention through efficient methods can help to reduce the mortality number. Medical data may contain many uncertainties, as they are fuzzy and vague in nature. Nonetheless, imprecise features data such as no values and missing values can affect quality of classification results. Nevertheless, the other complete features are still capable to give information in certain features. Therefore, an imputation approach based on Fuzzy C-Means and Particle Swarm Optimization (FCMPSO) is developed in preprocessing stage to help fill in the missing values. Then, the complete dataset is trained in classification algorithm, Decision Tree. The experiment is trained with Heart Disease dataset and the performance is analysed using accuracy, precision, and ROC values. Results show that the performance of Decision Tree is increased after the application of FCMSPO for imputation.