A Survey on Deep Learning Models Embed Bio-Inspired Algorithms in Cardiac Disease Classification

1. INTRODUCTION

People throughout the world have battled several deadly illnesses over the years, with heart disease [1] garnering the most attention in medical studies and has reached a great deal of observance in medical research. It is one of the prominent diseases that affect many people during their middle or old age, and in several cases, it eventually leads to fatal complications. Heart diseases are more habitual in men than in women. The diagnosis of heart disease [2] is very important in the prediction process. A timely diagnosis of heart disease is crucial in reducing health risks and preventing cardiac arrests. Some of the risk factors [3] faced by people that will rapidly increase heart diseases are smoking, gender (Sex), age, ethnicity, family history of the disease, high blood pressure, high blood cholesterol, diabetes, poor diet, lack of exercise, obesity, stress, and vessel inflammation. The World Health Organisation (WHO) [4] lists Cardiovascular diseases as the leading cause of death globally, with 17.9 million people dying every year. A pertinent threat endured by healthcare organizations, such as hospitals and medical centers, is the allocation of worthwhile amenities at moderate costs. Due to the lack of healthcare services from a physician, the need for an automatic diagnosis system emerged with artificial intelligence as a root concept, where then machine learning came into the picture with a lot of models focused on various human health-related problems. For more computational performance in diagnosing cardiac disease classification type, a contrast to traditional machine learning methods, deep learning technologies have recently emerged with support from bio-inspired algorithms that are mainly used for solving optimization problems.

The deep learning concept was first initiated by Walter Pitts and Warren McCulloch [5] in the year 1943 through a computer model that is based on the neural networks of the human brain. With the continuation of the existing model, Henry J Kelley [6] developed Back Propagation Model in 1960, and the simpler version of it was developed by Stuart Dreyfus [7] in 1962 and is based on the chain rule. Both of these models were inefficient and not be utilized in the research until 1985. An effort has been taken to develop deep learning algorithms by Ivakhnenko and Lapa [8] in 1965 with models of concept from polynomial activation functions. Later, in 1979, the first convolutional neural networks were used by Kunihiko Fukushima [9], who developed an artificial neural network that is named Neocognitron, which is used to recognize visual patterns. Neural networks and deep learning-based research emerged in the year 1985 to 1990, with the invention of the Support Vector Machine by Dana Cortes and Vladimir Vapnik [10] in the year 1995 and Long short-term memory in 1997, by Sepp Hochreiter and Juergen Schmidhuber [11]. With the adoption of Graphics Processing Units [12], there was a significant advancement in deep learning for the process of better and faster results. The influence of deep learning in the industry began in early 2000, with the vanishing gradient problem [13], and it was solved by layer-by-layer pre-training and LSTM. In 2009, Fei-Fei Li [14] launched ImageNet, a free database that is used for storing more than 14 million labeled images that are useful for training neural nets. Deep learning significantly held high efficiency and speed in the year 2011 and 2012, to the speed of GPUs [15] and with the invention of AlexNet, a CNN-based architecture developed by Krishevsky et al . [16]. Table 1 list the history of deep learning models. Deep learning has several other application areas, such as cybersecurity [17], the agriculture sector [18], and power expansion planning [19].

Bio means 'life' described from the Greek root word and contributes up mostly to terms from the domain of the 'life' sciences. Further from bio, the next important concept in biology (or bio) classes, which it guides to learn all about 'life.' Biological processes work with the way 'living' organisms function. A biography (or bio) is defined as a dissertation that tells all about the events in someone's 'life’, drafted by some writer other than the subject of the 'life' history. Bio-inspiration [20] is the blooming of modern structures, resources, and methods. It is stimulated by results found in adaptation, procedures, and salvation, which has biologically progressed over millions of years. The concept is to enhance the sculpting and replication of the biological system to better understand nature's analytical constructional features, such as an extension, for use in bioinspired design advancement. Biomimicry varies from bioinspiration by the replication of the designs in biological materials. Bioinspired can also be defined in research areas such as in the elegant ancestry of science: it is a glebe based on the remarkable functions by perceiving it to characterize living organisms and mimic those functions with the usage of abstraction. It has the strategy of solidity in the establishment of chemistry with the approach of substream. Notably, this research is still an emerging model with its technological and scientific systems, academics, and industrial levels. The bio-inspired field started in the early 1980s and 2010s with many types of research on the natural phenomenon in limited support, but in 2020 it was a huge growth in the variety of novel algorithms that have been defined successfully.

Complex optimization problems [21], such as designing topology, tuning and learning hyperparameters, can be solved efficiently using deep learning optimization with bio-inspired algorithms. Diagnosing diseases from many patients through the data collected with a different category of a specific disease is a classification problem. Robust, precise, and quick cardiac disease detection is crucial from a medical standpoint. Furthermore, it is a proven fact that, in comparison to ML approaches, DL-based cardiac disease identification offers generally better performance. Additionally, it has been noted that the majority of the articles have only used one dataset to evaluate the effectiveness of the various categorization methods. The conclusions drawn from the outcomes of one dataset might not apply to other datasets. As a result, it is necessary to test and assess the performance using at least two or more common datasets. For the Cardiac disease classification type, the data will be big, so with the support of deep learning methods embedded with bio-inspired algorithms, it will be able to classify it more accurately in diagnosing. Most of the early machine learning techniques predict various diseases type with accuracy suitable for small datasets [22]; for large datasets, deep learning optimization with a bio-inspired algorithm can predict it with more accuracy, which leads to the idea of embedding deep learning with bio-inspired algorithms for cardiac disease classification. Additionally, it has been noted that the ensemble version [23] of the top-performing model shows superior detection performance to other models. This motivates us to survey bio-inspired optimization algorithms with deep learning models for the classification of cardiac disease.

This paper mainly focused on research in cardiac disease with state-of-the-art deep learning models embedded with bio-inspired algorithms. The remainder of this paper is organized as follows: In section 2, elaborate information on cardiac disease is presented with deep learning methodologies performance in the prediction process. In Section 3, the related work about the various deep learning models with applications is listed. In Section 4, different datasets used by various researchers in different countries have been listed. In Section 5, deep learning benefits with embed on bio-inspired algorithms were listed for facing challenges in designing and training the deep networks for producing higher accuracy. In Section 6, the challenges of deep learning with bio-inspired algorithms and its future work are explained. Finally, in Section 7, the conclusion of the deep learning survey together with immanent trends that might be enhanced in the future, guide research towards health sectors in disease classification.

2. CARDIAC DISEASE: RESEARCH PROSPECT

Cardiac disease [24] has been a leading cause of death for over three decades. Approximately 18 million individuals drowned from Cardio Vascular Diseases [25] in 2019, depicting 30% in the world. Of these passing, 85% are because of heart failure and stroke. Most cardiovascular diseases can be abstained from pointing to the behavioral risk factors [26] of mankind, such as smoking, an unhygienic diet that leads to fatness, physical idleness, and the critical use of liquor. People with cardiac disease or who are at serious cardiovascular risk need a prior diagnosis and handle it through therapy and medications.

The side effects of cardiac disease [27] incorporate windedness, the shortcoming of the actual body, swollen feet, and exhaustion with linked signs, for instance, raised jugular venous pressing factor and fringe distension affected by practical cardiovascular or absence of cardiac irregularities. The examination strategies in the beginning phases used to distinguish Cardiac illness were confounded, and its subsequent intricacy is the significant cause that influences the norm of life. Cardiac disease determination and doctoring [28] are mind-boggling, particularly in non-industrial nations, because of the uncommon accessibility of symptomatic mechanical assembly and the lack of doctors and other assets which influence legitimate expectations and hospitalization of Cardiac patients. The precise and legitimate conclusion of cardiac disease trouble in patients is vital for diminishing their related chance of serious Cardiac complications and improving the protection of the heart. For this kind of challenge, deep learning models [29] can provide the best accuracy in detecting it priorly.

Nowadays, the health-related issue is a disaster for all human beings. Predicting, analyzing, and treatment purposes need so much cost, as well as for data handling process, is difficult. Since it creates a maximum portion of the information that medicos are incapable to deduce and use expertly, so Deep learning [30] has transpired as a more errorless and potent model in a voluminous kind of medical obstacles such as prognosis, prophecy, and invasion. Deep learning methods play a key role in classifying cardiac disease [31], which is a leading cause of death globally that can also be described in many ways by the researchers, such as coronary artery disease, cardiovascular disease, congenital heart disease, cardiomyopathy, and myocardial infarction.

To solve the complication of cardiac disease, various researchers developed machine learning and deep learning-based models that can be widely used in the prediction and diagnosis process. Latha C et al . [32] proposed ensemble classification techniques such as Naïve Bayes, Random forest and obtained an accuracy of 83.1%. Gong J et al . [33] used the Recurrent Attention Model for the prognosis of heart disease with an accuracy of 93.4%. Shiny Irene D et al . proposed Deep Belief Network and Extreme Learning Machine for heart disease prediction using hybrid fuzzy k-methods and achieved an accuracy of 97.62%. Garate Escamila et al . [22] used feature selection and principal component analysis and produced an accuracy of 98.7% for predicting heart disease. Beunza J et al . [34] proposed several machine learning models, such as decision trees, random forests, support vector machines, and neural networks, and classified them with an accuracy of 85%. The datasets used by the various researchers for the prediction of cardiac disease are from UCI Machine Learning Repository, Heart (Hungary), Heart (Swiss), Heart (Cleveland), CAD-Based Datasets, Statlog Heart Disease, Parkinson’s Disease (PD), Framingham Heart Study, and Clinical Data of Shanxi Academy of Medical Sciences. The various researchers utilized popular datasets for cardiac disease prediction and detection with several deep learning models with accuracy are listed in Table 2.

The important features used by the researchers of five different countries for the analysis of cardiac disease detection are shown in Fig. (1).

It is shown in Fig. (1). five different countries, such as India, the United States of America (USA), Russia, China, and the United Kingdom (UK), are the major 6 features they used for cardiac disease detection by Deep learning models.

3. DEEP LEARNING MODELS

Deep learning [48] is a subfield of machine learning technique that teaches you not just the relationship between two or more variables, but also the rules that govern that relationship as well as the logic behind it. The goal of deep learning [49] is to create higher-level information from lower-level information by learning the knowledge chunk at several levels of representation and abstraction. The deep learning concept was initiated from an analysis of artificial neural networks (ANNs) [50]. The word deep in deep learning refers to the number of layers through which the data is transformed to the next layer for the prediction process of the final output. The first layer of the deep learning network processes a preliminary data input and transfers it to the next layer as output. The second layer processes the previous layer’s information by enhancing additional information in a hidden way and transferring its result to the next layer. The next layer takes the second layer’s information, includes raw data of the user’s application, and makes the machine’s pattern even better. This continues across all levels of the neuron network, and finally, the output is predicted based on the given input features. Fig. (2) shows the architecture of the basic deep learning network. It first learns the primitive features followed by high-level features that help the building model so efficiently.

In this section, the various deep learning models, such as deep neural networks, convolutional neural networks, recurrent neural networks, extreme learning machines, deep belief networks, and transfer learning, are explained with their recent trends.

3.1. Deep Neural Network

Deep Neural Network [51] history was first initiated in year 1943 by Warren McCulloch and Walter Pitts that is dependent on the neural organizations of the human mind. Neural Networks can learn without help from anyone else and produce a yield that isn't restricted to the information given to them. The information is put away in its organizations rather than a data set, consequently, the deficiency of information doesn't influence its working. Deeper networks are efficient in terms of computation and the number of parameters. A neural network with some level of unpredictability, normally in two layers, qualifies as a deep neural network (DNN), or a deep net for short. Deep nets measure information in complex ways by utilizing refined mathematical modeling. The architecture of the deep neural network is shown in Fig. (3).

3.1.1. Advantages

1. It can learn by itself and produce an output that is not limited to the input provided to them.

2. The input is stored in its networks instead of a database and so the loss of data does not affect its working performance.

3. They can perform multiple tasks in parallel without affecting the system's performance.

3.1.2. Disadvantages

1. It requires a huge amount of data.

2. It is extremely computationally expensive and doesn’t have a strong theoretical foundation.

3.2. Convolutional Neural Networks

Convolutional neural networks were first introduced by Yann LeCun [52] in the year 1980. It is a type of neural network with one or more convolutional layers. It can automatically detect important features without the support of humans and is efficient in the computation process. It is performed by the concept of neurons that attain data from only a limited subdomain of the previous layer. CNN is used in the province of speech recognition and text classification for natural language processing and is used mainly for image classification problems. The architecture of the convolutional neural network is shown in Fig. (4).

3.2.1. Advantages

1. It has the type of sparse that extracts relevant features from the input dataset.

2. It utilizes an idea of parameter sharing that can be reused for many different functions

3.2.2. Disadvantages

1. Hyperparameter tuning is quite difficult in CNN.

2. It needs a large database for processing tasks which leads to more utilization of computing resources.

3.5. Deep Belief Network

Deep Belief Networks [55] were introduced in the year 2007 by Larochelle et al . as probabilistic generative models collected by stacked modules of Restricted Boltzmann Machines (RBMs) and provide an alternative to the discriminative nature of traditional neural nets. It can be used for identification, clustering, image processing, video sequels, signal-capture data, and also for training nonlinear autoencoders. The main characteristics of DBN are fast inference and the ability to encode higher-order network structures. The architecture of deep belief networks is shown in Fig. (7).

3.5.1. Advantages

1. It has the capability of learning features that can be done by a strategy of learning it layer-by-layer.

2. DBN can avoid the problem of overfitting and underfitting by implementing unlabelled data skillfully.

3.5.2. Disadvantages

1. It is not suitable in terms of performance when it is pre-trained by other models, mainly due to fixed input data.

2. DBN is quite complex in run time execution since it requires more time for it.

3.6. Transfer Learning

Transfer learning [56] was introduced in the year 1976 by Stevo Bozinovski and Ante Fulgosi. It is a famous methodology in deep learning where preceding models are utilized as the beginning stage of natural language processing and computer vision undertakings, given the immense register and time assets needed to create neural organization models on these issues. Transfer learning is prominent in deep learning for large datasets problems. It can be used in data-related research on image and language processing. It acts as an optimization process used to save time and result in better performance. The architecture of transfer learning is shown in Fig. (8).

Table 3.

Author	Application	Method/Algorithm	Year
A. Voulodimos, N. Doulamis et al . [57]	Computer Vision	Convolutional Neural Network	2018
M. Havaei et al . [58]	Brain Tumor Segmentation	Deep Neural Networks	2017
S. Purushotham, C. Meng [59]	Large Healthcare Datasets	Recurrent Neural Networks	2018
Ding S et al . [60]	EEG Classification	Extreme Learning Machines	2015
Sagar G. V [61].	Malware Detection on the Internet of Things	Deep Belief Networks	2019
Muhd Suberi A. et al . [62]	Automated Ischemic Classification in Posterior Fossa Ct Images	Transfer Learning	2019

3.6.1. Advantages

1. It speeds up the task of training the model on a new task by utilizing the pre-trained model and results in a good performance.

2. It has the power to learn information from simulations.

3.6.2. Disadvantages

1. It has the problem of negative transfer, which reduces the performance of the developed new model.

2. It has an overfitting problem due to the learning capability of the model in terms of noises from training data that impact negatively the final output.

Deep learning models’ recent trends and applications are displayed in Table 3. It shows that deep learning techniques can be used by researchers for various research applications.

The developed DL models yielded the most noteworthy characterization execution for Cardiac Disease in any event when utilizing boisterous ECG signals [63]. DL models can excerpt features, consequently forgoing the need to build feature extraction algorithms. They are strong to regular varieties, able to do hugely resemble calculations, and can undoubtedly be summed up for various issues [64]. At last, they can manage large information [65] and henceforth, can be utilized in the clinical field for exact and quick diagnosis. Despite that, DL techniques are as yet in the underlying phase of advancement. More tasks to compete in the future to analyze the design with a large data collection before medical use.

4. DATASET DESCRIPTION

Table 4. concisely delivers data from the Cardiac Disease datasets [66]. The table shows the major attributes of the datasets, such as the dataset name, samples, features, and country location. The most accepted Cardiac disease-associated dataset is the Cleveland dataset. Even though the entirety of the datasets has various kinds of highlights, a few of them are generally more significant for one or the other forecast or grouping. Henceforth, the general significance of highlights for the discovery of Cardiac Disease is examined. For instance, sex and age are examined independently because the effect of these two elements is major for research objectives in most sickness states.

First, it is well-accepted and not at all surprising that a higher level of age is associated with a greater chance of developing a cardiac illness, and this risk increases indirectly through time. Men experience the expansion in riskiness at a more youthful age, while pre-menopausal ladies are observed by physical changes, and just encounter the expansion in torment at the last stage. ECG data has an important role in cardiac disease classification. The most widely used tool is Electrocardiogram [101] which is mainly utilized for clinical diagnosis in cardiac disease identification. With this ECG classification, the major problem that can be identified, such as heartbeat segmentation, cardiac arrhythmia, and heart rhythms, will be detected or predicted with the support of artificial intelligence, machine learning, and deep learning models. The types of cardiovascular disease are hypertensive heart disease, rheumatic heart disease, valvular heart disease, cardiomyopathy, aortic aneurysms, heart arrhythmia, carditis, congenital, myocardial infarction, and heart failure. For patients with heart illnesses, an assortment of cardiovascular irregularities might happen, and these anomalies ordinarily lead to changes in ECG designs. Electrocardiography is the most well-known method for observing heart movement. Different heart problems can be distinguished by the examination of ECG signal anomalies. AI-enhanced ECG-based cardiovascular disease detection is explained in this [102] work that guides researchers to decide on clinical data limitations with the patients affected with cardiac disease. To enhance or boost the permanent accurate prediction value of cardiac disease for a large amount of data or features, the Bio-Inspired algorithm can be embedded with deep learning models.

5. DEEP LEARNING EMBEDDED WITH BIO-INSPIRED

A deep learning approach can be embedded with bio-inspired algorithms for the optimization process to enhance the accuracy of the predicted value. Before going to the depth of it, let’s start by defining the concept or idea behind bio-inspired computing or algorithm. Bio-inspired computing is a pivotal subgroup of natural reckoning. Bio-inspired computing (BIC) [103] spotlights the cabals and progress of computer algorithms and prototypes based on the biological armature and living phantasm. Bio-inspired computing [104] is a realm of study with biologically enthused quantifying and entreats to solve computer science challenges using biological instances. In general, it briefs social habits and emanation, but in the computer science field, bio-inspired computing revolutionizes with artificial intelligence and machine learning. Several areas of study in biologically inspired computing, with their biological counterparts, are Genetic algorithm with evolution, cellular automata with life, emanation with ants, bees, etc., neural networks with the brain, artificial experience with animation, the artificial immune system with the immune system, and learning classifier systems with cognition and evolution. Bio-inspired computing is a significant subsection of natural reckoning or nature-enthused algorithms. Plenty of real-world problems were solved by deploying nature-enthused algorithms which embrace concepts such as reproduction peripheral structures, shaggy systems, revolutionary assessing, and swarm cognizance [105].

Bio-Inspired computing [106] is divergent from conventional artificial intelligence by its framework to digital learning. Conventional artificial intelligence uses a creationist method, while bio-inspired computing uses an evolutionary approach. Bio-inspired computing [107] starts with a comprise of basic dictums, and creatures obey those commandments. Several times, these creatures have grown within simple impediments. This approach could be considered decentralized or bottom-up. Intelligence is constantly programmed with a programmer as a creator, which sometimes makes something unknowingly with the information in traditional computing. The two leading methods in BIAs include evolutionary algorithms [108] and swarm-based algorithms, which are accomplished by the adaptation of the natural and overall behavior of creatures accordingly. But still, based on the area of inspiration from nature to enhance a vaster view over the domain, it can be further refined to differentiate the accuracy of the algorithm. The taxonomy of bio-inspired optimization algorithms is shown in Fig. (9).

This section deals with twenty Bio-Inspired algorithms, namely, genetic algorithm, differential evolution, biogeography-based optimization, etc., from Evolutionary Algorithms (EAs) and ant colony optimization, particle swarm optimization, artificial bee colony, firefly algorithm, etc., from Swarm-based optimization algorithms.

Evolution's general meaning explains the method by which different categories of living organisms are reported to have developed from earlier forms during the history of the earth. So, the Evolutionary algorithm can be defined as a subset of its computation, which uses a framework inspired by biological evolution, for example, in the field of reproduction, mutation, recombination, and selection. It is based on a heuristics approach that can able to solve the problems that cannot be solved by polynomial time, such as the NP-hard problem. EA contains some steps such as initialization, evaluation, selection, reproduction, cross-over, and mutation. The steps shown above can provide an easy way to modularize the implementations of this algorithm category.

The notions of nature and biological activities have motivated the burgeoning of many enlightened algorithms for problem-solving. The problem-solving feature of artificial evolution is built into the selection process, which consists of two basic steps. 1) an evaluation of the phenotype that provides a quantitative score, also known as fitness value, and 2) a reproduction operator that makes a large number of copies of genotypes corresponding to phenotypes with high fitness values. These algorithms are commonly classified as evolutionary computation and swarm intelligence (SI) algorithms. Evolutionary computation is an idiom used to embellish algorithms that were enthused by the essence of the fittest. Swarm intelligence is an idiom used to embellish algorithms for disseminated problem-solvers that were enthused by the concerted team intellect of swarm or collective habits of bug enclaves and other species inhabitants.

Evolutionary algorithms are successfully applied to a lot of numerous problems from different areas or domains, including optimization, machine learning, research on operations, automatic programming, computational biology, and community structure. In a collection of distinct effects, the numerical function, which exhibits several problems, is not recognized, and the ideas at specific bounds are acquired by the method of replicas. Compared to other optimization strategies, an essential benefit of evolutionary algorithms is that they can cover multi-forming functions. Some of the benefits are enumerated as follows: It is notionally facile, flexible, and neutral in disparity with other numerical tactics. Invariance and evolutionary algorithms are worn to attune many results in modifying circumstances and environments. It can domicile challenges without support from human experts. EA has some disadvantages also: No assured optimized solution for some specific finite time. It delivers beneath a weak hypothetical basis concept. It uses parameter tuning. The cost of computation is prohibitive.

Swarm intelligence (SI) is a section of computational insight that discusses the conjunct habits emerging within an ascetic-organizing society of agents. Swarm Intelligence has absorbed the attention of reams of researchers on diverse concerns. Bonabeau cleared SI as “The embryonic collated sapience of clumps of facile envoys”. SI is the collaborative insight habits of ascetic-perspective and disseminated ways, e.g., mimicking teams of facile legates. Some instances of swarm-based intelligence are team hunting of communal pests, collaborative expedition, den structure of communal pests, communal grouping, and converging.

6. CHALLENGES AND FUTURE DIRECTIONS

Nowadays, Artificial Intelligence (AI) plays a significant job in medical care. Today’s world is severely affected by COVID-19, so the need for technology innovation through data analytics, AI, ML, and its sub-models of deep learning has been a major need in this pandemic. Another crucial area of AI that is effective at optimizing is evolutionary computing. Since ML, DL and EC are the most effective learning and optimization technologies, respectively, in the AI field, their advances are closely interwoven and considerably benefit from one another. The role of evolutionary computing [141] is important in data processing, model searching, training, evaluating, and utilizing for optimizing deep learning through bio-inspired optimization algorithms. Data mining and analytical modeling are now varsities for medical experts to learn about for future unknown diseases. The major challenges for deep learning are collecting data, working with interpretability and efficiency, integrating with other traditional methods, maintaining the imbalanced labels with multimodal data, and proposing new models with other interdisciplinary studies. To do this kind of work in the future, the deep learning method can be used for feature extraction, classification, and also with a combination of both. The important and powerful deep learning model, such as convolutional neural network (CNN), can be enhanced with more layers to develop neural architecture specifically for medical image analysis. The deep learning model can also be enhanced with novel data pre-processing, augmentation techniques, and hyperparameter optimization that will provide us with an additional performance from a network in terms of learning rate and dropout rate. These are the key aspects of successful deep-learning methods in medical care. In 2022, deep learning methods can be used for the implementation of projects by various companies, such as chatbots, forest fire prediction, digit recognition systems, image caption generators, traffic signs recognition, credit card fraud detection, movie recommendation system, virtual tracing system, drowsiness detection system and customer segmentation.

Future work will focus on the following points:

• Large-scale optimization for model training in medical datasets
• Evolutionary multitasking with the support of bio-inspired algorithms for optimization
• Optimization of new deep learning architectures in the healthcare sectors
• Multimodal optimization for deep learning ensemble models in the prediction of various human diseases
• The exploitation of problem-specific knowledge during the search for research papers in the medical field
• Exploration of alternative optimization domains for predicting diseases with more accuracy
• Inclusion of multiple implementations–related objectives, ideas, and motivation
• Reuse of learned knowledge towards modular learning in healthcare sectors

If we continue to work on the above challenges and future directions, deep learning models will accelerate breakthroughs across all medical-related issues with the applications of machine learning and artificial intelligence.

CONCLUSION

The technological evolution with the support of the Deep learning model in the Cardiac and medical fields guides humans to look over the AI-driven field. Data listed in the paper shows that deep learning has emerged as the latest top picker for researchers. Cardiac disease classification using deep learning models guides researchers to do more innovation by developing a new methodology. With the support of deep learning models, cardiac disease reviewed publication shows the comparison of different classification methods' accuracy rates in the detection or prediction process of it. The important features of the cardiac disease detection process from five different countries show that the researchers are utilizing it from several popular datasets, such as UCI (Cleveland, Hungary, Switzerland), Framingham Heart Study, Statlog Heart Disease, and Cardiovascular Health Study (CHS). The present study on deep learning models with cardiac disease indicates that the accuracy can be enhanced by embedding it through Bio-Inspired algorithms. Bio-inspired computing even has an immense reach to develop and spread across various research societies. The algorithms listed above must create a spacious impact on future-generation computing. It has been found that biocomputing provides a natural way to solve the problem of humans and lots of animals in nature.

In the future, Cardiac Disease classification with Deep learning and Bio-Inspired algorithm combination will be significantly increased. Deep learning is a fast-growing application of machine learning with the latest enhancement guides to do more research on predictive and diagnostic models that can save many patients and lead their life in a quality way. Also, with the emergence of novel deep learning methods, it can be proposed by the features of Cardiac Disease to predict Hypertrophic Cardiomyopathy (HCM) accurately at an early stage which is one of the Cardiomyopathy types. By doing this, it can save many numbers of lives and also deduct the cost problem for average-income people.

REFERENCES