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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.

Dendritic microstructures in cast Al-Si alloys - an analysis of dispersion factors

Warmuzek M.

Prace Instytutu Odlewnictwa

1998

R. 48, z. 1-2

55-73

The methods to evaluate the degree of dendritic structure dispersion in hypoeutectic Al-Si alloys were examined. The technique of direct measurement of dendrite arm and axis spacing was discussed and some criteria of the choice of stereological parameters used as a measure of this spacing were described. The results of measurements of the secondary dendrite arm spacing lambda2 and of the mean chord length and dendrite axis spacing were quoted. A statistical analysis of the results was made and some relationships exiting between the measured structural parametres were derived.

W pracy przeanalizowano metody oceny stopnia dyspersji struktury dendrytycznej w podeutektycznych stopach Al-Si. Omówiono metodę bezpośredniego pomiaru odległości ramion i osi dendrytów oraz kryteria doboru parametrów stereologicznych, stosowanych jako ich miara. Przedstawiono wyniki pomiarów odległości ramion dendrytów II rzędu lambda2 oraz długości średniej cięciwy i odległości osi dendrytów. Przeprowadzono statystyczną analizę wyników i wyznaczono zależności pomiędzy mierzonymi parametrami struktury.