Bat imperialist competitive algorithm (BICA) based feature selection and genetic fuzzy based improved kernel support vector machine (GF-IKSVM) classifier for diagnosis of cardiovascular heart disease

Nowadays rate of death is increased due to the rapid growth of cardiovascular diseases. Due to the above
reason, diagnosing the cardiovascular heart disease becomes very important in medical field. The subset
features which are considered as vital role in disease diagnosis are identified for the same disease in
modern medicine. Currently, many data mining techniques related to different types of heart disease
diagnosis were presented by several authors. Existing methods mainly concentrated on high accuracy
and less time consumption and it uses many different types of data mining techniques. This work
consists of three major steps such as missing data imputation, high dimensionality reduction or feature
selection and classification. The above steps are performed using a dataset called cardiovascular heart
disease dataset with 500 patients and 14 features and it utilizes several effective features. Because of
incomplete data collections Real time datasets often reveal unaware missing feature’s patterns. First step
consists of the Expectation Maximization (EM) algorithm which fits an independent component model
of the data. This increases the possibility of performing Modified Independent Component Analysis
(MICA) on imperfect observations. Bat Imperialist Competitive Algorithm (BICA) based feature
selection method is proposed to improve the dataset. BICA is an evolutionary algorithm which is based
on the development of human's socio-political. In this algorithm an m number of features and the N
number of cardiovascular heart disease observations are used as initial population which is called as
countries. A classification approach is introduced with Genetic Fuzzy based Improved Kernel Support
Vector machine (GF-IKSVM) classifier and a BICA based feature selection for the classification of
cardiovascular heart disease dataset. BICA is used for feature selection methods to reduce number of
features which indirectly decreases the important diagnosis tests required to the patients. The proposed
method achieves 94.4% accuracy, which is higher than the methods used in the literature. This GFIKSVM
classifier is well-organized and provides good accuracy results for cardiovascular heart disease
diagnosis.