Flower Pollination Neural Network For Heart Disease Classification

Yazid, Mohamad Haider Bin Abu; Talib, Mohamad Shukor; Satria, Muhammad Haikal

doi:10.1088/1757-899x/551/1/012072

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…Additionally, FPA was applied to update the Probabilistic Neural Network (PNN) weights in classification problems and produced better results than PNN on all 11 datasets [7]. Yazid www.ijacsa.thesai.org et al [15] used FPNN, which combines FPA and PNN, to classify heart diseases and found it to have higher accuracy than a standard backpropagation neural network based on results from four UCI datasets.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Multi-Layer-Perceptron Training using Quadratic Interpolation Flower Pollination Neural Network on Non-Binary Datasets

Polly¹,

Hartati²,

Suprapto³

et al. 2023

IJACSA

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Machine Learning (ML) algorithms are widely used in solving classification problems. The biggest challenge of classification lies in the robustness of the ML algorithm in various dataset characteristics. Quadratic Interpolation Flower Pollination Neural Network (QIFPNN) is categorised into ML algorithm. The new QIFPNN's extraordinary capabilities are measured on binary-type datasets. This research ensures that the remarkable ability of QIFPNN also applies to non-binary datasets with balanced and unbalanced data class characteristics. Flower Pollination Neural Network (FPNN), Particle Swarm Optimisation Neural Network (PSONN), and Bat Neural Network (BANN) were used as comparisons. The QIFPNN, FPNN, PSONN, and BANN were used to train Multi-Layer-Perceptron (MLP). The test results on five datasets show that QIFPNN obtains an average classification accuracy higher than its comparison in three datasets with balanced and unbalanced data class characteristics. The three datasets are Iris, Wine, and Glass. The highest classification accuracy obtained by QIFPNN in the three datasets is 97.1462%, 98.6551%, and 73.1979%, respectively. Based on the F1-score test from QIFPNN, it is higher than all the comparisons in four datasets: Iris, Wine, Vertebral column, and Glass. Sequentially, 96.4599%, 98.7155%, 90.7517%, and 60.2843%. It proves that QIFPNN can also classify datasets with non-binary data types with balanced and unbalanced data class characteristics because they are more consistently tested on various datasets and are not susceptible to the influence of variations in dataset characteristics so that they can be applied to various types of data or cases.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach to Multi-Layer-Perceptron Training using Quadratic Interpolation Flower Pollination Neural Network on Non-Binary Datasets

Polly¹,

Hartati²,

Suprapto³

et al. 2023

IJACSA

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KNN-based approach for the classification of fusarium wilt disease in chickpea based on color and texture features

Hayit,

Endes,

Hayit

2023

Eur J Plant Pathol

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A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest

El-Shafiey

Hagag

El‐Dahshan

et al. 2022

Multimed Tools Appl

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Nowadays, heart diseases are significantly contributing to deaths all over the world. Thus, heart-disease prediction has garnered considerable attention in the medical domain globally. Accordingly, machine-learning algorithms for the early prediction of heart diseases were developed in several studies to help physicians design medical procedures. In this study, a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) optimized approach based on random forest (RF), called GAPSO-RF, is developed and used to select the optimal features that can increase the accuracy of heart-disease prediction. The proposed GAPSO-RF implements multivariate statistical analysis in the first step to select the most significant features used in the initial population. After that, a discriminate mutation strategy is implemented in GA. GAPSO-RF combines a modified GA for global search and a PSO for local search. Moreover, PSO achieved the concept of rehabbing individuals that had been refused in the selection process. The performance of the proposed GAPSO-RF approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the receiver operating characteristic (ROC) curve by using two datasets from the University of California, namely, Cleveland and Statlog. The experimental results confirm that the GAPSO-RF approach attained the high heart-disease-prediction accuracies of 95.6% and 91.4% on the Cleveland and Statlog datasets, respectively. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods.

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Flower Pollination Neural Network For Heart Disease Classification

Cited by 6 publications

References 4 publications

A Novel Approach to Multi-Layer-Perceptron Training using Quadratic Interpolation Flower Pollination Neural Network on Non-Binary Datasets

A Novel Approach to Multi-Layer-Perceptron Training using Quadratic Interpolation Flower Pollination Neural Network on Non-Binary Datasets

KNN-based approach for the classification of fusarium wilt disease in chickpea based on color and texture features

A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest

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