Liver Disease Prediction Using Ga Feature Selection, Social Spider Optimization, And Cnn Classification
Keywords:
Genetic Algorithm (GA), Social Spider Optimization (SSO), Parameter Tuning, Convolutional Neural Network(CNN), Dimensionality Reduction, Liver Disease, Prediction, Diagnosis, Computational Efficiency, Benchmark Datasets, Predictive Accuracy, Hierarchical Representations.Abstract
The project on "Liver Disease Prediction using GA
Feature Selection, Social Spider Optimization, and
CNN Classification" presents an advanced and
integrated approach to predict liver diseases. By
combining Genetic Algorithm (GA) for feature
selection, Social Spider Optimization for parameter
tuning, and Convolutional Neural Network (CNN) for
classification, the system aims to enhance the accuracy
and efficiency of liver disease prediction.Liver disease
is a significant health concern globally, necessitating
accurate and timely diagnosis for effective treatment.
In this study, we propose a novel approach for liver
disease prediction using Genetic Algorithm (GA)
feature selection, Social Spider Optimization (SSO),
and Convolutional Neural Network (CNN)
classification. The proposed method aims to enhance
predictive accuracy by efficiently selecting relevant
features from complex datasets and optimizing the
CNN architecture for improved classification
performance. The GA-SSO framework is employed to
select an optimal subset of features from a
comprehensive set of potential predictors, reducing
dimensionality and enhancing the efficiency of
subsequent classification. The selected features are
then utilized to train a CNN model, leveraging its
ability to automatically extract hierarchical
representations from raw input data. Experimental
results on benchmark liver disease datasets
demonstrate the effectiveness of the proposed
approach, outperforming existing methods in terms of
predictive accuracy and computational efficiency.
