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Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

Kumar Parvathaneni Rajendra, Ravichandran Suban, Narayana Satyala

Bio-Algorithms and Med-Systems

2021

Vol. 17, no. 2

119--136

Objectives: This research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification. Methods: As the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with CanberraDistance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)- based feature reduction approachis deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier. Results: An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions: From the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively. Results: Finally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.

Measures of central tendency of aquatic habitat parameters : application to river macrophyte communities

Szoszkiewicz K., Kayzer D., Staniszewski R., Dawson H.

Polish Journal of Ecology

2010

Vol. 58, nr 4

693-706

The aim of this paper is to assess usefulness of different measures when determining central tendencies which characterise the environmental requirements of living organisms. Mathematical analyses were made of the environmental parameters of river macrophyte communities which were taken as a representative pattern of different levels of biological structures. To deliver a representative dataset, botanical surveys were carried out on a range of British rivers together with environmental assessment and the plant communities groups were identified based on characteristic species according to eslished phytosociological criteria. The mean values and standard univariate medians of the revealed associations were compared with means calculated on the basis of advanced transformation and also with the rarely calculated multivariate L median. Due to high variance and asymmetrical distribution, the analyses based on the mean-value appeared to be limited in application. To avoid this disadvantage transformation to obtain normality of the dataset standardisation was proposed although even this did not fully reach a satisfactory symmetry. It was concluded that each environmental variable for each single biota must be individually treated by a suile transformation to obtain approximately normal distributions. The univariate median was very resistant to the effects of outliers but gave a flattened output of the environmental dataset making the partitioning of biological units very difficult. The multivariate L median appeared to be unaffected by outliers. It enabled to obtain considerable ordering of communities against individual environmental parameters without data transformation.