=Paper=
{{Paper
|id=Vol-3682/Paper15
|storemode=property
|title=Detection of Heart Failure Using Swarm Intelligence
|pdfUrl=https://ceur-ws.org/Vol-3682/Paper15.pdf
|volume=Vol-3682
|authors=Avni,Arunima Jaiswal,Amritaya Ray,Swati Gola
|dblpUrl=https://dblp.org/rec/conf/sci2/AvniJRG24
}}
==Detection of Heart Failure Using Swarm Intelligence ==
https://ceur-ws.org/Vol-3682/Paper15.pdf
Detection of Heart Failure Using Swarm Intelligence
Avni 1,*, Arunima Jaiswal 1, Amritaya Ray 1 and Swati Gola 1
1 Computer Science & Engineering Department, Indira Gandhi Delhi Technical University for Women, Delhi, India
Abstract
Heart disease remains a significant health concern globally. In this study, we propose an
innovative approach by combining the Sparrow Search Algorithm (SSA) with deep learning
techniques, including Long Short- Term Memory (LSTM), Bidirectional LSTM (BI-LSTM),
and Gated Recurrent Unit (GRU) networks. The UCI Cleveland Heart Disease dataset is
utilized for evaluating the performance of the suggested hybrid algorithms. We can reach
an accuracy up to 97.86% with BI-LSTM. The results indicate promising outcomes in terms
of accuracy and computational efficiency. This convergence of swarm intelligence and
healthcare has the potential to transform medical care, cut costs, and save lives, presenting
a significant advancement in predictive medicine.
Keywords:
Heart Disease, Deep Learning, Swarm Intelligence, Feature Selection, Hyperparameter Tuning, Sparrow
Search Algorithm, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Unit,
Mealpy, Tensorflow.
1. Introduction
Heart failure is a very significant public medical concern globally, posing substantial challenges to
healthcare systems due to its high prevalence, mortality rates, and associated healthcare costs. Early
and accurate prediction of heart failure is crucial for effective patient management, timely
interventions, and improved clinical outcomes. In recent years, the integration of advanced machine
learning techniques with healthcare data has shown promising results in enhancing predictive models
for heart failure prognosis. [1]
This research paper explores the application of Sparrow Search Algorithm (SSA), a novel
metaheuristic optimization algorithm which is inspired by the sparrows’ behavioral foraging nature [2],
combined with three deep learning (DL) techniques, namely Long Short-Term Memory (LSTM),
Bidirectional- LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU) for heart failure prediction. The chief
objective of this comparative research is to investigate the efficiency of SSA in optimizing DL models to
accurately predict heart failure risk.
Symposium on Computing & Intelligent Systems (SCI), May 10, 2024, New Delhi, INDIA
∗ Corresponding author.
† These authors contributed equally.
avni065btcse20@igdtuw.ac.in (Avni); arunimajaiswal@igdtuw.ac.in (A. Jaiswal);
amritaya014btcse20@igdtuw.ac.in (A. Ray); swati025btcse20@igdtuw.ac.in (S. Gola)
0000-0002-4530-9904 (J.Sarda); 0000-0003-2759-3224 (A.Bhoi); 0000-0002-6376-8346(D. Garg)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Heart failure is a very significant public medical concern globally, posing substantial challenges to
healthcare systems due to its high prevalence, mortality rates, and associated healthcare costs. Early
and accurate prediction of heart failure is crucial for effective patient management, timely
interventions, and improved clinical outcomes. In recent years, the integration of advanced machine
learning techniques with healthcare data has shown promising results in enhancing predictive models
for heart failure prognosis. [1]
This research paper explores the application of Sparrow Search Algorithm (SSA), a novel
metaheuristic optimization algorithm which is inspired by the sparrows’ behavioral foraging nature [2],
combined with three deep learning (DL) techniques, namely Long Short-Term Memory (LSTM),
Bidirectional- LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU) for heart failure prediction. The chief
objective of this comparative research is to investigate the efficiency of SSA in optimizing DL models to
accurately predict heart failure risk.
The motive of this research is to develop robust and reliable predictive models that leverage both
the optimization capabilities of SSA and the representational power of DL techniques to enhance heart
failure prediction accuracy. By leveraging large-scale healthcare datasets, this study is targeted to
enhance the advancement of predictive analytics in healthcare, facilitating early identification of
individuals at risk of heart failure and enabling proactive intervention strategies to mitigate adverse
health outcomes.
2. Dataset Description
The UCI Cleveland Heart Disease dataset is a widely used dataset for heart disease prediction and
classification. It contains 1025 instances and 14 attributes, including both categorical and continuous
variables. Some of the dataset attributes are Sex, Age, Fasting Blood Sugar, Serum Cholesterol(chol),
Chest Pain Type (CP) and many others. [3]
This dataset is of great utility in creating and validating models for forecasting the probability of
heart disease, relying on a patient's characteristics. It has been widely employed in healthcare-focused
projects involving machine learning and data analysis.
3. Literature Review
The proposed Swarm-ANN strategy, developed by Sudarshan Nandy, introduces an innovative
approach to cardiovascular disease prediction by leveraging swarm intelligence for neural network
optimization. The strategy involves the random generation of Neural Networks, multiple stages of
weight changes, and a heuristic formulation for weight adjustment. While achieving an impressive
accuracy of 95.78% on a benchmark dataset, the research may be limited in its generalizability due to
the focus on specific dataset characteristics and predefined parameter ranges for learning rates and
population sizes. This could pose a risk of overfitting to the peculiarities of the chosen dataset, raising
questions about the strategy's adaptability to diverse datasets and real-world scenarios. [4]
The presented MLP-PSO Hybrid Algorithm, introduced by Ali Al Bataineh and Sarah Manacek,
contributes significantly to healthcare by leveraging machine learning for enhanced heart disease
prediction. The study acknowledges the challenge of developing heart disease due to multiple
underlying factors. Utilizing the Cleveland Heart Disease dataset, the suggested MLP-PSO hybrid
algorithm demonstrates superiority over 10 different ML algorithms, achieving a notable accuracy of
84.61%. However, the research does not explicitly address potential limitations, leaving room for
further exploration into the algorithm's scalability, robustness, and performance across varied datasets
and clinical settings. [5]
The methodology proposed by Shahrokh Asadi, merging multiple objective particle swarm
optimization (MOPSO) as well as Random Forest for the prediction of heart disease, addresses the
challenges associated with traditional diagnostic methods. The fusion of evolutionary multi-objective
optimization and Random Forest aims to produce diverse and accurate decision trees simultaneously,
demonstrating promising results with an accuracy of 88.26% on the Statlog dataset. Comparative
analyses across six heart datasets showcase the superiority of the proposed method, emphasizing its
potential to outperform conventional Random Forest algorithms with different classifiers.
Nevertheless, potential loopholes may include a lack of in-depth analysis of algorithmic complexities
and scalability concerns across different datasets. Addressing these aspects could further validate the
proposed methodology's effectiveness and reliability in diverse clinical applications. [6]
The newly proposed heart disease prediction model, QPSO-SVM, by E. I. Elsedimy, Sara M. M.
AboHashish, and Fahad Algarni, showcases innovation through the integration of quantum-behaved
particle swarm optimization (QPSO) and support vector machine (SVM). While achieving a remarkable
accuracy of 96.31% on the Cleveland heart dataset, potential loopholes may include the need for
comprehensive exploration of QPSO-SVM's computational efficiency and scalability, especially when
applied to larger datasets or in resource-constrained environments. Further investigation into these
aspects can enhance the model's applicability and robustness. [7]
The Deep Edge Intelligence-based solution by Hossain and Tabassum employs the OQFFN algorithm
on a Raspberry Pi, ensuring real-time heart failure predictions in IoT-based healthcare. The approach
enhances reliability without constant network stability, making it unique in comparison to cloud-based
services. Evaluation shows OQFFN's superior accuracy and efficiency at the edge, with potential
applications in Ambient Assisted Living. Despite success, limitations in edge processing for complex
algorithms are acknowledged, highlighting the need for future developments in distributed prediction
models. The research significantly contributes to enhancing IoT-based healthcare systems. [8]
4. Methodology
Our principal methodology of implementation is outlined as follows:
Explore Python Packages for Genetic and Evolutionary Algorithms: The research began by exploring
various Python packages for genetic algorithms (GAs) and evolutionary algorithms (EAs), such as DEAP,
PyGAD, and Genetic Algorithm Python. Further evaluation involved assessing each package based on
factors like functionality, ease of use, documentation, and community support.
Implement Mealpy Package in Python: We implemented the Sparrow Search Algorithm (SSA) from
the Mealpy package, to aid in feature selection. It was done by implementing genetic, swarm-based and
evolutionary patterns followed by sparrows moving in a swarm using Python capability for
optimization tasks.
Feature Selection Using Sparrow Search Algorithm: We studied the SSA algorithm and its associated
mathematical formulae to change the bias, weight and threshold values applied in the code so that it
provides optimal feature selection for our dataset.
Apply DL models like LSTM, Bi-LSTM, and GRU: We chose the deep learning (DL) models, LSTM,
BiLSTM, and GRU, based on the nature of our data and task requirements. Implemented these models
using TensorFlow and PyTorch in Python. We also used libraries like Keras for easier implementation.
Further data preprocessing was done, including normalization and sequence padding. We then trained
each DL using hyperparameters that were fine-tuned using SSA for optimized performance.
Compare the Results: We compared the results by assessing the performance metrics. We visualized
the results using plots to present comparisons between different models and algorithms.
Figure 1. Building a Swarm Algorithm Enhanced Deep Learning Model for Heart Prediction on the
UCI Cleveland Dataset
4.1 Data Preprocessing
In the pursuit of optimal performance and robustness in deep learning models, meticulous attention
to data preprocessing methodologies is imperative. Data preprocessing serves as a critical precursor to
model training, facilitating enhanced model generalization and efficacy by mitigating the deleterious
effects of noise, imbalance, and irregularities inherent in raw data. Preprocessing steps commonly entail
data normalization to standardize feature scales, imputation techniques for handling missing values,
and encoding categorical variables to numerical representations. Additionally, feature selection or
extraction techniques may be applied to reduce dimensionality and enhance model interpretability. In
the context of our investigation, comprehensive data preprocessing procedures were meticulously
executed, including outlier detection and removal to attenuate the influence of aberrant data points,
and stratified sampling to alleviate class imbalance concerns. Subsequently, the preprocessed data were
subjected to rigorous cross-validation to ascertain model performance and generalization capabilities.
These meticulous preprocessing endeavors culminated in superior model performance metrics and
bolstered the veracity of our findings.
4.2 SSA in Feature Selection
In the context of feature selection, the Sparrow Search Algorithm (SSA) incorporates mathematical
formulations that enable the optimization of feature subsets based on objective functions designed to
evaluate their relevance and discriminative power. SSA employs mathematical expressions to model
the movement of individual sparrows within the feature space, with each sparrow representing a
potential feature subset. The algorithm utilizes mathematical operators such as random walks, levy
flights, and local search mechanisms to explore and exploit the solution space efficiently. Furthermore,
SSA employs fitness functions that quantitatively assess the quality of feature subsets based on criteria
such as classification accuracy, information gain, or other relevant metrics. These fitness functions
guide the optimization process by assigning higher scores to feature subsets that contribute positively
to the performance of the machine learning model. [10][11]
4.3 SSA in Hyperparameter Tuning
In the realm of hyperparameter tuning, the Sparrow Search Algorithm (SSA) offers a versatile and
efficient approach to optimize the configuration settings of machine learning models. SSA operates by
iteratively exploring the hyperparameter space, represented by individual sparrows, and updating their
positions based on fitness evaluations. In the context of hyperparameter tuning, SSA dynamically
adjusts hyperparameter values to maximize model performance on a validation dataset. This involves
formulating an objective function that quantifies the model's performance based on chosen evaluation
metrics such as accuracy, loss, or cross-validation scores. SSA optimizes hyperparameters by iteratively
evaluating different configurations, seeking to minimize the objective function. Through a combination
of exploration and exploitation, SSA efficiently searches for optimal hyperparameter settings, adapting
its search strategy based on the observed performance of candidate solutions. By leveraging SSA for
hyperparameter tuning, researchers can automate the process of optimizing model configurations,
thereby improving model performance, generalization capabilities, and computational efficiency. [12]
4.4 Sparrow Search Algorithm
The Sparrow Search Algorithm (SSA) is a recently introduced metaheuristic optimization algorithm
inspired by the cumulative foraging behavior of sparrows in searching for food. It is categorized with a
family of swarm intelligence algorithms, which mirror the social behavior of organisms to solve complex
optimization problems. SSA operates based on the concept of exploration and exploitation, where
individual sparrows in the population search for optimal solutions through a combination of random
exploration and local exploitation of promising regions in the search space. [13]
From a research perspective, the Sparrow Search Algorithm offers several notable characteristics
that make it appealing for solving optimization problems. Firstly, SSA exhibits strong global search
capabilities, allowing it to efficiently explore the solution space and locate potential optima across a
wide range of problem domains. This attribute is particularly advantageous for addressing high-
dimensional and non-convex optimization problems commonly encountered in various scientific and
engineering fields. [14]
Secondly, SSA incorporates adaptive mechanisms to dynamically adjust its search behavior based on
the efficacy of solutions generated during the optimization process. This adaptability enables the
algorithm to effectively balance exploration and exploitation, thereby enhancing its convergence speed
and solution quality over successive iterations.
Moreover, the use of adaptive parameters reduces the reliance on manual parameter tuning, making
SSA more user- friendly and accessible to researchers and practitioners. [15]
4.5 Application of Deep Learning Models
4.5.1 The Gated Recurrent Unit (GRU):
It is a simplified recurrent neural network (RNN) architecture adept at capturing temporal
dependencies in serialized data. GRU operates by employing gating mechanisms to modify the
network's hidden state during each time step, controlling the flow of information within the network.
[16]
It comprises of two gating mechanisms where the reset gate decides the extent to which the prior
hidden state should be disregarded, whereas the update gate governs the degree to which the new input
influences the hidden state's update. Subsequently, the GRU's output is determined based on the
modified hidden state. With its update and reset gates, GRU efficiently retains and updates hidden
states, making it suitable for tasks like heart failure prediction. Its streamlined design and adaptability
enable effective modeling of both short-term fluctuations and long-term patterns in patient data.
Additionally, the streamlined design of GRU reduces the risk of overfitting and enables faster
convergence during training. [17]
4.5.2 Long Short-Term Memory (LSTM):
LSTM represents a specific architecture within recurrent neural networks (RNNs) tailored to tackle
the complexities associated with capturing prolonged dependencies in sequential data. In contrast to
conventional RNNs, LSTM incorporates distinct memory cells, allowing the network to preserve
information across extended temporal intervals. This design renders LSTM particularly adept at
handling tasks involving sequential data, including but not limited to time series prediction, language
processing, and healthcare analytics. [18]
The fundamental elements of an LSTM unit comprise the input gate, forget gate, output gate, and cell
state, each playing a crucial role in managing information flow within the network. The input gate
regulates what information should be stored in the cell state, while the forget gate determines which
information should be discarded from the cell state. Through the cell state, the LSTM unit retains
information over time, facilitating the capture of long-term dependencies. Lastly, the output gate
governs which information should be passed from the cell state to the subsequent time step. Renowned
for its efficacy in modeling temporal dependencies and mitigating challenges such as vanishing
gradients, the LSTM has garnered widespread adoption across diverse domains. [19]
4.5.3 Bidirectional Long Short-Term Memory (Bi-LSTM):
It is an expansion of the classic Long Short-Term Memory (LSTM) design, aiming to grasp context
from both past and future in sequential data. It is composed of two LSTM layers: one handling the input
sequence forwards and the other backwards. This bidirectional approach allows the model to grasp
dependencies from both preceding and succeeding time steps, thereby improving its comprehension of
temporal sequences and predictive accuracy. [20]
In Bi-LSTM, each hidden state incorporates input not just from the past but also from the future,
enabling the model to adeptly handle long-term dependencies. This bidirectional method proves
particularly advantageous in tasks necessitating context from both past and future, such as natural
language processing, speech recognition, and time series analysis. The Bi-LSTM architecture comprises
forward and backward LSTM layers linked to a dense layer, amalgamating insights from both directions
prior to making predictions. By harnessing information from both preceding and succeeding contexts,
Bi-LSTM models excel in capturing intricate patterns in sequential data, outperforming unidirectional
LSTM models. [21]
5. Result and Discussion
In this paper, we conducted experiments that are aimed to investigate the effectiveness of Sparrow
Search Algorithm (SSA) combined with three Deep Learning (DL) models, namely Long Short-Term
Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM),
for heart failure prediction. Among these models, Bi-LSTM emerged as the most accurate predictor of
heart failure risk with the accuracy of 97.86%, while GRU and LSTM got 82.92% and 97.50%
respectively. As for the models that were not optimized by swarm techniques, showed comparatively
less accuracy i.e. 77.17%, 89.75% and 90.73% were the results of the DL models GRU, LSTM and BI-
LSTM respectively.
The results revealed that all the swarm integrated models outperformed various normal deep
learning models in terms of predictive accuracy, precision and recall. Among all the swarm optimized
DL models with SSA, Bi-LSTM showed the best result i.e. 97.86% accuracy. It’s ability to grasp both past
and future sequential data proved to be improving the ability of the model to understand the complex
temporal patterns underlying heart failure progression. This bidirectional processing enabled Bi-LSTM
to leverage information from both preceding and succeeding time steps, leading to more accurate
predictions compared to unidirectional models.
The integration of Sparrow Search Algorithm (SSA) with DL models significantly enhanced the
predictive performance of Bi-LSTM. SSA effectively fine-tuned the various parameters of the Bi-LSTM
model, enabling it to achieve superior accuracy in heart failure prediction tasks. The optimization
process facilitated the exploration of the solution space and the identification of optimal model
configurations, leading to improved generalization and robustness.
While Bi-LSTM demonstrated remarkable accuracy in heart failure prediction, further research is
warranted to explore its applicability in real-world clinical settings. Future studies could focus on
evaluating the interpretability of Bi-LSTM models, exploring the influence of different input features on
predictive performance, and conducting prospective validation studies to assess the model's clinical
utility. Additionally, exploring ensemble techniques and hybrid models combining DL with other
machine learning approaches could further enhance predictive accuracy and robustness.
Table 1. Tabulation of accuracy achieved by applying various DL models on results generated without
SSA
DL Models GRU LSTM Bi-LSTM
Accuracy (%) 77.17 89.75 90.73
Precision (%) 81.03 87.27 88.18
Recall (%) 91.26 93.20 94.17
Performance Metrics of DL Models
100
90
80
70
60
50
40
30
20
10
0
GRU LSTM Bi-LSTM
Accuracy without SSA (%) Precision (%) Recall (%)
Figure 2. A bar plot visualizing the performance metrics of each Deep Learning model without any
optimization
Table 2. Tabulation of accuracy achieved by applying various DL models on results generated with SSA
DL Models SSA-GRU SSA-LSTM SSA-Bi-LSTM
Accuracy (%) 82.92 97.50 97.86
Precision (%) 78.68 76.22 81.30
Recall (%) 93.20 90.29 84.46
Performance Metrics of SSA Optimized
DL Models
100
90
80
70
60
50
40
30
20
10
0
GRU LSTM Bi-LSTM
Accuracy of SSA (%) Precision (%) Recall (%)
Figure 3. A bar plot visualizing the performance metrics of each SSA optimized Deep Learning model
Accuracy Comparison
100
90
80
70
60
50
40
30
20
10
0
GRU LSTM Bi-LSTM
Accuracy of SSA (%) Accuracy without SSA (%)
Figure 4. A bar plot visualizing the comparative accuracy derived from each Deep Learning model executed
along with and without Sparrow Search Algorithm
6. Conclusion
In conclusion, this research paper investigated the application of Sparrow Search Algorithm (SSA)
combined with three deep learning (DL) models, namely Long Short-Term Memory (LSTM), Gated
Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM), for heart failure
prediction. Through extensive experimentation and evaluation on real-world healthcare datasets, we
have demonstrated the effectiveness of the proposed approach in accurately predicting heart failure
risk.
The results obtained from our experiments highlight the significant impact of optimization
techniques such as SSA in fine-tuning DL models for improved predictive performance. We observed
that the combination of SSA with DL techniques not only enhanced the predictive accuracy of heart
failure prediction models but also contributed to better generalization and robustness.
Furthermore, our findings underscore the importance of selecting appropriate DL architectures for
healthcare analytics tasks. While LSTM, GRU, and Bi-LSTM all exhibited promising results, Bi-LSTM,
with its ability to capture both past and future context in sequential data, emerged as the most effective
model for heart failure prediction in our experiments.
Overall, the outcomes of this study have implications for clinical practice, offering healthcare
practitioners a valuable tool for early detection and risk stratification of heart failure patients. By
leveraging the synergy between optimization algorithms like SSA and advanced DL models, we can pave
the way for more accurate, efficient, and personalized healthcare interventions, finally progressing
towards an improved patient health results and boosted quality of care in the management of heart
failure.
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