Related Experiment Video
Updated: Dec 10, 2025
Anesthesia-free Heartbeat Measurements in Freely Moving Zebrafish
Published on: April 18, 2025
Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm
Renji P Cherian1, Noby Thomas2, Sunder Venkitachalam3
1Professor, Department of Computer Science & Engineering, Vimal Jyothi Engineering College, Chemperi, Kannur, India.
Insights
This study introduces a novel hybrid algorithm (PM-LU-NN) for accurate heart disease prediction, outperforming existing machine learning models. The new method enhances clinical data analysis for better patient outcomes.
Area of Science:
- Cardiology and Clinical Data Analysis
- Machine Learning in Healthcare
- Computational Intelligence
Background:
- Heart disease is a leading cause of mortality and morbidity globally.
- Accurate prediction of heart disease is crucial for effective clinical management.
- Increasing healthcare data complexity challenges traditional analysis and prediction models.
Purpose of the Study:
- To propose a novel heart disease prediction model using machine learning.
- To enhance prediction accuracy through advanced feature extraction and dimensionality reduction.
- To optimize the prediction model using a hybrid meta-heuristic algorithm.
Main Methods:
- Feature extraction using statistical and higher-order statistical methods.
- Dimensionality reduction via Principal Component Analysis (PCA).
- Heart disease prediction using a Neural Network (NN) optimized by a hybrid Particle Swarm Optimization (PSO) and Lion Algorithm (LA) update (PM-LU) approach.
Main Results:
- The proposed PM-LU-NN model demonstrated superior accuracy compared to conventional methods.
- Accuracy improvements were 3.85% over LM-NN and PSO-NN, 12.5% over WOA-NN and FF-NN, and 7.41% over LA-NN.
- The hybrid algorithm effectively addressed the optimization challenges in NN weight tuning.
Conclusions:
- The PM-LU-NN model offers a significant advancement in heart disease prediction accuracy.
- Hybrid meta-heuristic optimization is effective for enhancing machine learning-based clinical prediction models.
- This approach can aid in managing complex e-healthcare data for improved patient care.
Abstract:
Heart disease remains one of the significantcauses ofmortality and morbidity amongst the world's population. Predicting heart disease is considered as one of the vital issues in clinical data analysis. Since the number of data is rising gradually, it is muchcomplicatedforanalyzing and processing, and especially, it becomes difficult to maintain the e-healthcare data. Moreover, the prediction model under machine learning seems to be anessentialfacet in this research area. In this scenario, this paper aims to propose a new heart disease prediction model with the inclusion of specificprocesses like Feature Extraction, Record, Attribute minimization, and Classification. Initially, both statistical and higher-order statistical features are extracted under feature extraction. Subsequently, the record and attribute minimization carried out, where Component Analysis PCA plays its major role in solving the "curse of dimensionality."Finally, the prediction process takes place by the Neural Network (NN) model that intake the dimensionally reduced features. Moreover, the major intention of this paper deals with the accurate prediction. Hence, it is planned to influence the utility of meta-heuristic algorithms for the weight optimization of NN. This paper introduces a new hybrid algorithm termed Particle Swarm Optimization (PSO) merged LA update (PM-LU) algorithm that solves the above-mentioned optimization crisis, which hybrids the concept of Lion Algorithm (LA) and PSO algorithm. Finally, the efficiency of proposed work is compared over other conventional approaches and its superiority is proven with respect to certain performance measures. From the analysis, the presented PM-LU-NN scheme with regards to accuracy is 3.85%, 12.5%, 12.5%, 3.85%, and 7.41% better than LM-NN, WOA-NN, FF-NN, PSO-NN and LA-NN algorithms.
