Extended Firefly Optimization Model for Heart Disease Prediction using Hybrid Classifier Approach

Problem Definition

In the realm of cardiovascular disease diagnosis using machine learning techniques, researchers have been exploring various classifiers such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Among these, ANN has been identified as the most efficient in making accurate predictions. However, a key limitation exists in the fact that these classifiers do not always provide efficient results for every type of dataset, which can impact the diagnosis process. This is particularly evident when researchers train and test their classifiers on the commonly used UCI heart disease dataset or data obtained from affordable hospitals. The reliance on a limited set of data sources, such as the UCI dataset, may restrict the generalizability of the results and limit the ability to accurately predict cardiovascular diseases across different populations.

Moreover, the existing machine learning algorithms used for classification can be further enhanced by increasing the number of attributes in the dataset. By leveraging a richer set of attributes, the accuracy of predictions can be improved, leading to more reliable diagnostic outcomes. However, enhancing the scalability and precision of the forecasting scheme requires further investigation and research. Thus, there is a clear need for advancements in the field of cardiovascular disease diagnosis through machine learning, with a focus on addressing the limitations of existing classifiers and exploring opportunities for improvement in prediction accuracy and scalability.

Objective

The objective of this work is to enhance the accuracy of predicting heart diseases through a hybrid model combining artificial neural networks (ANN) and firefly optimization algorithm. By optimizing the weight values of ANN using the firefly algorithm, the proposed model (fa-ANN) aims to address the limitations of existing classifiers and improve the classification accuracy for different types of healthcare datasets. The step-by-step approach involves data collection, applying multiple classifiers, selecting the best one based on accuracy, optimizing it with firefly optimization, retraining the network, and comparing the results with traditional classifiers. This novel approach seeks to leverage the strengths of ANN and the optimization capabilities of the firefly algorithm to achieve more precise predictions of cardiovascular diseases.

Proposed Work

The proposed work aims to address the limitations of existing classification techniques in predicting heart diseases by introducing a hybrid model of artificial neural network (ANN) and firefly optimization algorithm. The objective is to enhance the accuracy of the ANN by optimizing its weight values through the application of the firefly algorithm. This approach is based on the literature survey which highlighted the need for an algorithm that can consistently provide optimal results for different types of healthcare datasets. By hybridizing the ANN with firefly optimization, the proposed model (fa-ANN) will potentially improve the classification accuracy of the system. The step-by-step working plan involves collecting input data from the UCI dataset, applying multiple classifier algorithms, selecting the best classifier based on accuracy, optimizing the selected classifier using firefly optimization, retraining the network, and comparing the results with traditional classifiers.

This novel approach combines the strengths of ANN with the optimization capabilities of the firefly algorithm to achieve more accurate predictions of heart diseases.

Application Area for Industry

This project can be used in various industrial sectors such as healthcare, finance, agriculture, and manufacturing. In the healthcare sector, the proposed solution of combining the best classifier (ANN) with firefly optimization can significantly improve the accuracy of diagnosing cardiovascular diseases. By optimizing classifier factors, the system can provide more precise predictions, leading to better patient outcomes. In the finance industry, this project can be used for fraud detection and risk assessment, where accurate classification and prediction are crucial for making informed decisions. In agriculture, the optimized classifier can help in crop yield prediction and disease detection, enabling farmers to take proactive measures to improve productivity.

In the manufacturing sector, the hybrid model can be utilized for quality control and predictive maintenance, ensuring smooth operations and reducing downtime. Overall, the benefits of implementing these solutions include enhanced accuracy, improved decision-making, and increased efficiency across various industrial domains.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of machine learning and healthcare data analysis. By combining the traditional classifier with the firefly optimization technique, researchers, MTech students, and PHD scholars can explore innovative research methods to improve the accuracy of classification and prediction in healthcare datasets. This project's relevance lies in addressing the limitations of existing classifier techniques and data mining methods in healthcare data analysis. By leveraging the hybrid model of traditional classifiers like SVM, KNN, and Random Forest with the firefly optimization technique, the proposed work aims to provide an optimal solution for accurate prediction of cardiovascular diseases. Researchers can use the code and literature of this project to enhance their understanding of optimization algorithms in machine learning and explore the potential applications in healthcare data analysis.

By studying the impact of optimization on traditional classifiers like ANN, researchers can develop more efficient prediction models for diagnosing various diseases. The project's future scope includes further research on enhancing the scalability and precision of the proposed hybrid model, as well as exploring the application of optimization techniques in other domains of healthcare data analysis. With the increasing availability of healthcare datasets, the proposed methodology can be extended to different types of healthcare data to improve the accuracy of classification and prediction. Overall, the proposed project offers a valuable contribution to academic research by providing a framework for integrating optimization techniques with traditional classifiers in healthcare data analysis. Through hands-on experience with the code and methodology, students and researchers can explore new avenues for innovative research methods, simulations, and data analysis within educational settings.

Algorithms Used

The proposed work aims to enhance the accuracy of healthcare data classification by combining the artificial neural network (ANN) algorithm with the firefly optimization algorithm (FA). The project involves collecting input data from the UCI repository, applying four different classifier algorithms (SVM, ANN, KNN, and Random Forest), selecting the best classifier based on accuracy, and then optimizing the selected classifier using the firefly optimization algorithm. This hybrid model, referred to as fa-ANN, leverages the strengths of both the ANN classifier and the firefly optimization technique to improve classification accuracy. The final results from the proposed model will be compared with traditional classifier approaches to evaluate the effectiveness of the hybrid model.

Keywords

SEO-optimized keywords: Artificial Neural Network, Heart Disease Prediction, Firefly Optimization Algorithm, Weight Tuning, Predictive Model, Machine Learning, Heart Disease Diagnosis, ANN Optimization, Optimization Algorithms, Heart Disease Detection, Heart Disease Diagnosis, Heart Disease Prediction Model, Heart Disease Risk Assessment, Heart Disease Classification, ANN Performance Improvement, Predictive Accuracy, UCI dataset, SVM, KNN, Random Forest, Machine Learning for Healthcare, Hybrid Model, Classification Algorithms, Health Experts, Cardiovascular Diseases, Scalability, Precision, Forecast Scheme, Data Mining, Healthcare Data, Classification Accuracy, Optimization Techniques, Initial Weights, Input Data, Traditional Approach

SEO Tags

Artificial Neural Network (ANN), Heart Disease Prediction, Firefly Optimization Algorithm, Machine Learning, Healthcare Data Analysis, UCI Heart Disease Dataset, Classifier Techniques, SVM, KNN, Random Forest, Weight Optimization, Prediction Accuracy, Healthcare Data Mining, Diagnosis Improvement, Predictive Model, ANN Performance Enhancement, Heart Disease Detection, Research Methodology, Classification Algorithms, Hybrid Model Development, Data Training, Prediction Model Comparison