Classification model for heart disease prediction with feature selection through modified bee algorithm

Abstract

Now-a-days a healthcare eld produces a huge amount of data, for processing those data some e cient techniques are required. In this paper, a classi cation model is developed for heart disease prediction and the attribute selection is carried out through a modi ed bee algorithm. The prediction of heart disease through models will help the practitioners to make a precise decision about patient health. Heart disease dataset is obtained from the UCI repository. Dataset consists of 76 features and all those seventy-six features have not contributed equal information during the time classi cation. In the entire attributes, some of the attributes have contributed a large amount of information at the time of classi cation and some of the attributes have contributed only a small amount of information during the classi cation task. In this paper, a modi ed bee algorithm is used to identify the best subset of features from the entire features in the dataset i.e., in the training phase of classi cation only retain those features that are contributing more information during classi cation and it will reduce the training time of classi ers. The experiment is analyzed with a obtained reduced subset of features by using the following classi ers such as Support Vector Machine, Navie bayes, Decision tree and Random forest.The experimental result shows that the Support Vector Machine classi er will provide a good classi cation accuracy, true positive rate, true negative rate, false positive rate and false negative rate compared to Navie bayes and Random forest tree classi er.Engineering and Technology (IJCIET), 10(1)

Special issue on soft computing for edge-driven applications

“…The third section discusses about the real-time applications of soft computing-driven models by: developing an adaptive neuro-fuzzy inference system to monitor and manage the soil quality in order to build a sustainable farming culture (Remya 2022); introducing a supervised learning-based techniques for predicting the seed germination ability in a precision farming environment (Yasam et al 2022); incorporating an improved sine cosine optimization algorithm for achieving an efficient color image segmentation (Mookiah et al 2022); enhancing singlechannel speech quality and intelligibility under multiple noise conditions by using Wiener filter and deep CNN (Hepsiba and Justin 2021); introducing modified bee algorithm to efficiently classify and predict heart diseases (Velswamy 2021); utilizing the characteristics recognition and soft multimedia system for performing Japanese machine translation and edge-driven hardware implementations (Song 2021); utilizing soft computing-driven social network analysis approach to trace the fake news propagation path (Sivasankari and Vadivu 2021); designing and developing a novel deep learning approach for assisting a gait-based fall prediction model (Murthy et al 2021); and designing and developing a novel generative adversarial networks [GAN] model for generating a high-quality sign language (Natarajan and Elakkiya 2021).…”

Comparative analysis of weka-based classification algorithms on medical diagnosis datasets

“…With the continuous development of cross-cutting disciplines, a large number of machine learning and artificial intelligence algorithms are applied to medical datasets as an important component in disease prediction models, demonstrating excellent performance in medical-related fields such as disease prediction and assisted diagnosis, drug selection and application, and health insurance fraud and detection [1][2][3][4]. As important artificial intelligence techniques such as data mining and machine learning, the decision tree algorithms in Weka are often used in basic medical research areas such as classification of DNA genes, classification and comparison of DNA barcodes, development and implementation of siRNA design tools, screening and differentiation of salt-loving and non-salt-loving proteins, and differentiation of classification properties of cell death-related proteins [5][6][7][8][9].…”