Support vector machine based disease classification model employing hasten eagle Cuculidae search optimization

Abstract

SummaryThe enormous growth in the field of medicine utilizes machine learning for the well‐being and robustness of human beings. The emerging rate of the disease due to urbanization or various other global facts increases the mortality rate of the individual every year. The detection and classification of disease at the proper time helps an individual to be alive and healthy. Various methods of machine learning are accomplished to classify the diseases, but there is a necessity for the enhancement in the classification methods. In this research, the disease classification is performed using the proposed hasten eagle Cuculidae search optimization algorithm based support vector machine classifier (HECSO‐SVM) with the hybrid kernel function. The feature extraction with the statistical and correlation features presents the effective feature vector for classification and the curse of dimensionality is handled using the proposed HECSO algorithm. Initially, the data will be collected and preprocessing is performed and the features are extracted and selected using the optimization techniques. The proposed method effectively reduces the computation time and increases the accuracy of classification. The analysis is made based on the four different datasets such as Parkinson, heart, lung, and hepatitis that obtains an accuracy rate of 94.582%, 94.100%, 93.985%, 94.582%.