A Novel Hyper-Spectral Model to Optimize the Prediction Rate for Heart Disease in Modern Healthcare Networks ^†

Abstract

Coronary heart disease is one of the most extreme and leading causes of death globally. Heart disease’s correct and timely prognosis is crucial to prevent further morbidity and mortality. In recent years, the appearance of present-day healthcare has led to analyzing and improving the latest diagnostic models for heart ailments. Hyper-spectral imaging methods are rising as appropriate and reliable techniques for heart ailment prediction. This paper presents an optimized hyper-spectral model for coronary heart disorder prediction, which uses both depth and spatial features. The proposed version extracts depth and spatial features to recognize heart ailments in clinical scans. The depth function extraction layer is designed to phase the scans and become aware of suspicious areas with anomalies. The spatial feature extraction layer is designed to obtain the features in those areas for similar spatial analysis. The extracted functions are then used to train a primarily CNN-based version on the type of coronary heart disorder. The effects of the proposed version were tested and compared with other existing methods and determined to be correct. The proposed model is robust and presents a greater accuracy and better overall performance for heart disorder analysis.

1. Introduction

In recent years, healthcare professionals and researchers have looked for new ways to predict heart disease among patients. Innovations in medicine have led to the development of sophisticated predictive algorithms, and advanced diagnostic tests are now available. This is an important step forward in modern healthcare as it allows doctors to intervene early and mitigate the risk of heart-related conditions and subsequent mortality [1]. The most popular form of prediction is the Framingham Risk Score, which uses gender, age, blood pressure, cholesterol levels, and smoking status to calculate the likelihood of having, or developing, heart disease within a 10-year period. Other methods include imaging tests such as echocardiography and CT scans to identify blockages in the arteries, as well as genetic testing to analyze a person’s specific genes and flag any anomalies that may be related to heart health. Using this technology, healthcare providers are able to accurately detect disease, allowing them to tailor lifestyle and treatment plans accordingly [2]. For instance, if someone is considered to be at high risk, then they may be put on medication such as statins or prescribed a stricter diet, or they may be monitored more closely with periodic check-ups. This is an effective way of reducing the risk of heart disease and its associated mortality rate. The development of predictive algorithms and advanced diagnostic tests has drastically improved the ability of healthcare professionals to predict the risk of heart diseases. This has allowed them to intervene early and provide treatments that mitigate the risk of heart-related conditions and mortality [3]. Heart-related mortality occurs across the globe and is steadily increasing with the increase in aging populations and unhealthy lifestyles. Predictive health solutions provide real-time, predictive information to inform healthcare professionals and patients about potential health risks like heart diseases. The use of predictive health solutions for heart disease prediction in modern healthcare is of great value in providing proactive approaches to care for patients and reducing the mortality rate due to heart diseases. Predictive analysis in modern healthcare helps healthcare experts and patients effectively manage health risks [4]. Predictive analysis allows healthcare professionals to use data-driven models to accurately predict the progression and outcome of diseases such as heart diseases. Additionally, it can be used to identify which patients are more likely to develop them and need to be monitored more closely for heart diseases. A construction diagram is shown below in Figure 1.

Predictive health systems integrated with modern healthcare systems can help to identify the exact treatments and interventions that a patient needs in order to reduce their risk of heart disease or further complications from ongoing heart disease [5]. This will not only help reduce the mortality rate due to heart disease but also improve the lives of patients at risk of the disease. It will enable healthcare professionals to deliver more targeted, effective, and long-term prevention plans for patients with a higher risk of developing heart diseases [6,7]. Modern healthcare solutions for heart disease prediction are paramount in providing timely, highly accurate, and personalized solutions to patients at risk of heart disease and thus can go a long way in improving taking care of heart disease patients. The main contributions of the research have been the following:

• Enhancing Early Diagnosis and Treatment: Heart disease prediction helps to identify risk factors and initiate preventive measures to reduce the risk of developing heart complications. Early diagnosis and treatment enable healthcare professionals to take proactive action to protect and care for patients’ heart health.
• Improving Quality of Care: Heart disease prediction improves the quality of healthcare provided as it allows doctors to track any changes in the patient’s health over time. This helps to identify and address any problems quickly before they become larger concerns.

2. Materials and Methods

With the advancements in modern healthcare and technology, heart disease prediction has become a prominent concern within the medical realm [8]. This is based on a comprehensive evaluation of various data, such as physical features and medical information, in combination with other factors that may influence the risk of developing cardiovascular conditions. Certain devices can detect changes in the body which may indicate possible cardiovascular issues [9]. For example, a continuous glucose monitor can alert healthcare providers to any changes in blood sugar levels, which may signal a greater risk of morbidity from heart disease. Despite the advances that have been made in the prediction of heart disease, there is still much to be materialized in order to ensure that individuals receive timely preventative care [10]. As is so often the case, the best outcome is prevention, but current approaches to diagnosing and treating heart disease usually focus on identifying and treating issues after they have manifested, leaving many individuals at risk of future health issues [11,12,13]. Furthermore, there is some evidence to suggest that major risk factors, such as genetic predisposition, lifestyle habits, and environmental factors, are all interconnected, and taking into consideration all these factors is necessary to accurately identify risk and prevent problems before they arise [14]. Another issue faced by modern healthcare when it comes to predicting heart disease is the disconnect between medical providers and patients. Patients may need to take a more proactive approach in terms of monitoring their own health, but may be unaware of the potential risks or lack the resources to seek out proper medical advice [15,16]. There can be a lack of coordination between different healthcare providers. It is important that data on peoples’ cardiovascular health be more widely shared among various medical and healthcare providers. Sharing means collecting and storing the data so that each provider has comprehensive access to it [17]. Predicting heart disease in modern healthcare requires a more comprehensive, integrated, and individualized approach that incorporates key risk factor assessments and better communication and collaboration among medical practitioners [18,19].

Modern healthcare has seen the emergence of various artificial intelligence and ML techniques for accurate and reliable results [20]. Additionally, there is potential for modern healthcare to benefit from advanced data techniques and streaming analytics for accurate heart disease diagnosis.

2.1. Proposed Model

This model can help doctors and healthcare professionals better assess the risk of heart disease in their patients in order to make more informed decisions on treatment and management. Hyper-spectral image data allow for more accurate and detailed assessment of tissue and organ functionality than traditional imaging techniques.

This is a powerful technique used to enhance the performance of a model by leveraging pre-trained models or features extracted from other medical imaging tasks. It allows for the transfer of knowledge from one task to another, and the pre-trained models/features can help reduce the amount of training data and computational resources required to train a model. In the context of medical imaging, transfer learning could be used to incorporate knowledge from other imaging tasks into the model. Pre-trained models or extracted features from these datasets could be used to initialize the model weights or provide additional high-level knowledge to the model. This could help reduce the amount of training data required or enable the model to learn more complex features, ultimately leading to improved performance. The optimized hyper-spectral model for heart disease prediction in modern healthcare includes several steps.

$$dC=d{C}^d-1$$

(1)

$$d{D}^c=dC+1$$

(2)

$$dD=\ln (c+1)$$

(3)

The wavelengths or spectral bands used in hyper-spectral imaging for heart disease prediction typically include the near-UltraViolet (UV), visible, and Near-Infrared (NIR) portions of the electromagnetic spectrum. These bands are chosen because they provide spectral information that can highlight differences in tissue composition related to a range of diseases, including heart disease. The near-UV portion of the spectrum (below 400 nm) is particularly useful for identifying certain elements in the blood and cardiac tissue, which can be indicative of plaque formation. Similarly, the visible and NIR spectra can be used to detect cellular changes due to inflammation and indicators of fibrosis. Furthermore, the narrow spectral bands used in hyper-spectral imaging allow for a finer spectral resolution than traditional complementary metal oxide sensors, enabling more accurate diagnosis of heart disease. A functional block drawing is shown below in Figure 2.

$$d(c)=e^d\ast \underset{d\to 0}{\lim }\left(\frac{1-e^d}{c}\right)$$

(4)

$$d^{″}(c)=e^d\ast \underset{c\to 0}{\lim }\frac{c}{\ln (c+1)}$$

(5)

$$d^{″}(c)=e^d\ast \underset{c\to 0}{\lim }\frac{1}{\frac{1}{c}\ln (c+1)}$$

(6)

$$d^{″}(c)=e^c\ast \underset{c\to 0}{\lim }\frac{1}{\ln {(c+1)}^{\frac{1}{c}}}$$

(7)

The shape of the spectral features used to build the model depends on the particular form of heart disease being analyzed.

2.2. Operating Principle

As an innovative approach to aiding in the early detection of cardiac disease, this model uses advanced spectral imaging technology to accurately measure the cardiovascular disease biomarkers in a patient’s bloodstream.

$$d^{″}(c)=e^c\ast \frac{1}{\ln \ast \underset{c\to 0}{\lim }{(c+1)}^{\frac{1}{c}}}$$

(8)

Spatial feature extraction is the process of deriving meaningful patterns and structures from spatial data using a machine learning algorithm. Typically, this involves the extraction of features from an image or another type of spatial data source. It can help make sense of complex data. One of the most commonly used methods of spatial feature extraction is convolutional layers. Convolutional layers are used to process input images. The filters each extract a single feature from the image, such as edges or corners. This output is then “pooled” to reduce the feature dimensionality and reduce the computational load. A popular pooling method is max-pooling, where the maximum value of each feature within a given region is taken and incorporated into the feature vector. By extracting these spatial features and pooling them, the model can identify the most relevant patterns and structures within the identified suspicious areas. This makes it easier for the model to recognize important objects and fine details, which can then be used to make decisions.

2.3. Functional Working

Modern healthcare has seen the emergence of new, innovative approaches to predicting and diagnosing diseases, including the use of optimized hyper-spectral models.

$$d^{″}(c)=e^c\ast \frac{1}{\ln c}$$

(9)

$$d{d}_1=-dd+{\displaystyle \sum _{c=1}d{d}_c=0\Rightarrow \frac{d{d}_c}{d{c}_d}=1}$$

(10)

The proposed hyper-spectral model utilizes unique algorithms and techniques to differentiate between normal and abnormal heart tissue based on the extracted depth features. The model extracts 3D-morphable metric features that describe the shapes and differences in the structure of normal and abnormal heart tissue. These features include size, shape, surface area, orientation, and volumetric shape descriptors. Additionally, the model applies a series of mathematical algorithms, such as maximum-entropy-guided belief propagation and multidimensional scaling analysis, to learn and classify differences between normal and abnormal tissue structures. The model utilizes novel machine learning techniques to further differentiate between normal and abnormal tissues based on the extracted depth features.

3. Results and Discussion

The proposed Optimized Hyper-Spectral Model (OHSM) has been compared with the existing Lion-Based Butterfly Optimization (LBBO), Multi-Variate Heart Disease Optimization (MHDO), Hybrid Machine Learning Algorithms (HMLAs), and Hybrid Classifier-Based Federated Learning (HCFL).The improved accuracy and performance of the hyper-spectral model is significant in reducing false negatives and false positives in heart disease prediction. This can potentially increase the efficacy of patient care, as false negatives can delay the diagnosis of heart disease or provide a false sense of security, while false positives can lead to unnecessary tests and treatment. With the improved accuracy of the hyper-spectral model, clinicians can better identify patients at risk of heart disease and provide the appropriate care in a timely manner.

3.1. Computation of Accuracy

The accuracy of an optimized hyper-spectral model for heart disease prediction in contemporary healthcare may be determined by measuring the number of proper effective and poor authentic results when compared to a standard reference or floor truth.

Figure 3 shows a comparison of accuracy. In a computation cycle, the proposed OHSM reached 98.53% accuracy. The existing LBBO achieved 72.13%, MHDO reached 45.49%, the HMLA reached 80.03%, and HCFL obtained 65.18% accuracy.

3.2. Precision

The precision of an optimized hyper-spectral model for heart disease prediction in modern healthcare is a measure of the model’s ability to correctly identify cases of heart disease among a population.

Figure 4 shows a comparison of precision. In a computation cycle, the proposed OHSM reached 99.27% precision. The existing LBBO achieved 80.33%, MHDO reached 69.20%, the HMLA reached 96.79%, and HCFL obtained 56.65% precision.

3.3. Recall

Recall is the potential of a model to properly perceive a fine elegance. In the case of a heart sickness optimized hyper-spectral model, this is taken into account and calculated by dividing the quantity of actual positives via the overall number of instances wherein coronary heart disease became present.

Figure 5 shows a comparison of recall. In a computation cycle, the proposed OHSM reached 91.19% recall. The existing LBBO achieved 42.21%, MHDO reached 39.57%, the HMLA reached 47.07%, and HCFL obtained 86.11% recall.

Strategies such as using powerful processors, distributed computing, and the optimization of algorithms can help reduce the computational resources required for implementing the hyper-spectral model and mitigate the potential challenges related to processing power, memory, and time complexity.

• Processing Power: Hyper-spectral models involve a large amount of data, which typically requires a powerful and efficient processor to manage these data. A powerful processor is essential to reduce the data processing and time complexity. A multi-core processor can also be beneficial for high-end applications.
• Memory: Hyper-spectral models involve a large amount of data, which requires ample memory. The use of a portable hard drive or other external storage devices can be helpful in managing large volumes of data. Additionally, cutting-edge technologies such as distributed computing can help reduce time by utilizing more than one machine to process the data.
• Time Complexity: Hyper-spectral models require a lot of computing resources and take time to process. Adopting methods such as parallel computing can reduce the time complexity significantly, as it splits the task into consecutive tasks that can be processed simultaneously. Additionally, optimizing algorithms can help increase the processing speed.

It is important to note that the use of additional resources such as cloud computing, GPUs, and dedicated hardware may be necessary for more resource-intensive tasks. The interpretability of the model’s predictions can be examined by exploring how the depth and spatial features contribute to the identification of specific cardiac abnormalities. The depth at which an abnormality is detected by the model allows the physician to determine the origin of the lesion, whereas spatial features allow the physician to discern the exact shape and size of the abnormality. By combining these two features, the model can provide a more comprehensive and accurate diagnosis. The diagnostic accuracy of the model is further improved by the use of deep learning techniques, which allow it to detect more subtle differences between healthy and abnormal tissue. The interpretability of the model’s predictions can also aid clinicians in understanding the diagnosis. By providing a more holistic view of the cardiac abnormality, the model can improve physician’s decision-making by highlighting the important features that can be used to differentiate between healthy and abnormal tissue. This can further improve physicians’ confidence and accuracy when making a diagnosis or recommendation for treatment. Additionally, the interpretability can help physicians to better explain the results to patients and can provide guidance in deciding whether an intervention is truly necessary.

4. Conclusions

An optimized hyper-spectral model for heart disease prediction in modern healthcare is a machine learning approach that utilizes hyper-spectral imaging data for the early detection of cardiovascular disease. Practical applications of this model require further validation and clinical testing to confirm its accuracy and effectiveness. This could involve collecting real-world patient data and running the model against them to measure the predictive accuracy. Additionally, the model could be tested in simulation scenarios with clinical experts providing input on the ground truth of the interventions and outcomes. Lastly, real-world clinical trials involving patient populations could be conducted to evaluate the efficacy of the model and compare it to the current standard of care. Ultimately, further clinical testing and research is needed to determine the ability of the proposed model to improve clinical outcomes and patient outcomes. Such research could help inform new clinical pathways and provide more comprehensive personalized treatments, which can individualize care and help patients achieve better outcomes. The future scope of an optimized hyper-spectral model for heart disease prediction in modern healthcare is very promising. This model could be implemented in a variety of settings, including hospitals, clinics, and other healthcare settings.