The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The model performance is verified with a full and minimum set of features. Fewer features enhance the processing time of the classification process while the accuracy is compromised. The performance of classifiers with less features has been analyzed with experimental results. The proposed system is built on the Internet of Things Platform for medical data for the classification process which aids medical professionals to detect heart diseases through cloud platforms. The results accuracy is matched to classical learning models such as Convolutional Neural Network (CNN), Deep CNN, and neural ensemble models. The analysis of the proposed diagnostic system can determine the heart disease risks efficiently. Experimental results demonstrate that flexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.
Heart disease is the primary cause of death in the elderly age group. Hence, the medical sector needs to enhance the prediction of heart problems [
Deep learning models, for heart disease Prediction, are the main aspect of our research [
The authors in [
The authors in [
Authors in [
The authors in [
Authors in [
The authors in [
To solve these problems, in this paper, we propose a deep learning model (DCNN) [
This paper demonstrates the developed DCNN model to identify cardio disorders and enhance detection accuracy utilizing deep learning classification models. The classification model proposed for this research has two stages:
A deep learning model that motivated a multilayer regulation. Further, the deduced pattern is utilized to predict if the patients have cardio disorders based on a learning model.
The performance is measured for precision via error probability, accuracy, and specificity [
The scope of the research is depicted as follows:
Determine the performance of heart disease detection using an assisted deep learning model,
Develop a classification technique using the Bayesian model to attain the best error rate,
Develop a perceptron method with multiple layers of neurons, with hidden layers for binary classification.
The experiments employed the dataset (DS1) in [
The rest of the paper is divided as follows: In Section 2, The deep learning model (DCNN) is proposed to aid and enhance patient precision and reliability in prognostics of cardio diseases. In Section 3: The experiments are depicted and tested. Section 4 depicts the conclusions.
In this paper, we propose a DCNN model for the timely prediction of cardiovascular disease and early diagnosis. The dataset DS1 [
where
The score for class x as an average pooling for the final function is computed using the weight of the forwarding linear combination of activation vector, which aids to analyze various windows such as, diagnosis, observation, and prediction. In particular, the model computes the gradient score of class x (Gx) regarding the P which is the averagepooled global value to produce the weights
where,
This method was utilized in the differentiation of the activation map x, defining the value of the activation function at the temporal position
A Bayes net is a probability model using a prediction model graphically. Bayes nets are dedicated to statistical distributions to predict the heart disease diagnosis. Features that are defined by Bayes net, are depicted in
The Bayes net is defined as a group of feature variables represented as a directed graph depicting the conditional variable dependencies. The edges in the Bayes graph depict the dependent variables (features, while the nodes that are not connected by edges then these nodes are independent variables.
Let’s define M as a variable that depends on n attributes M = {A1, A2,…, An}. Let a hypothesis (H) that M is an element to a class C. The probability (HM), is defined as P(HM). P(HM) is the posterior conditional probability of the M condition on H. The posterior conditional probability can be predicted via the Bayes graph theorem.
As depicted in
The Bayesian classification is based on the probability Bayes theorem. This is a specific case of the Bayes net and a Softmax classifier using the following features: age, sex, and other hypertension attributes. The functions are autonomous in the conditionally Bayes net. Accordingly, the adjustments of one feature do not impact the others. The Naïve model is proper for dataset classification of high dimensions. The classifier is characterized that the attribute value being separate from the significance of the other attributes of a class.
Let’s then define
hence,
if the attributes are independent of each other therefore:
where,
Initialize all weights and all biases in N, where N depicts the Network 
Do { 
For each training item M in T { 
For each input layer, i { 

For every hidden layer i { 

For each 



For every i in the output layer 


For a weight 


Endfor 
For each bias 


EndFor 
Endfor 
While (The threshold is greater than 0.003) 
} 
As depicted in Algorithm 1, the perceptron with multiple layers’ algorithm consists of neurons, and hidden multilayers for the binary classification modeling. For each convolution neuron, a perceptron algorithm utilizes activation. Therefore, the functions perception modeling is presented as biological neuronal models, which utilize the perceptron activation layers in neurons. The activation formula defines the weights for the neuron inputs and decreases the count of layers to two or three layers, by changing the weights that are allocated to a perceptron neuron.
The hypothesis, in our research, has a mathematical model that is derived from the prognosis formula,
The prediction was passed from patient precharge data from clinical factors
As depicted in
The likelihood formula for cardiac occurrences has been proposed in our research. It should be noted that, there has been no substantial increase in the cardiac occurrences probability per day. The prediction of the continuing cardiac occurrences likelihood per day is defined as follows:
As depicted in
The survival curve is formulated as depicted in the following equation,
A mathematical formula is defined that accurately computes the likelihood probability of the hospitalized patient’s medical results who are discharged after correct diagnosis. The likelihood probability of potential cardiac occurrences can be predicted by utilizing the current data. The DCNN model has been employed on the Medical Platform (IoT), which helps clinicians to diagnose cardiac patient’s cases in the cloud medical platform efficiently and the experimental results prove that a flexible model with hyperparameters can attain an accuracy of 97.11%.
The Internet of Things IoT medical platform for cardiac disease prediction. Recent research has presented that Internet of Things sensors are successfully incorporated into different paradigms, especially emergent health care, where the IoT Medical platform (IoTM) is usually employed. IoTM monitors heart pulse, hypertension, oxygen levels, and glucose readings using an oximeter. our paper proposes an Internet of Things medical platform that employs fitness tracking to collect medical data of a patient using blood pressure, heart pulse, and ECG data. The platform can drive signals with complete data to healthcare physicians handing accurate profile of healthcare. This paper proposes an effective approach for the modern busy world. The test results are discussed in next section.
The experiments are performed employing the dataset (DS1) which includes data about elderly patients with heart diseases in [
This research employs a deep learning model for cardiac diseases risk factor prediction and risk classification. It pursues to enhance the classification accuracy of cardio disease risk with an ensemble model. Ensemble classification model delivers higher precision and flexibility, even for unstructured data, than traditional classification. The proposed DCNN architecture is a beneficial tool in heart disease recognition for medical doctors. An extra phase of feature selection was presented to enhance accuracy. The accuracy is calculated as follows:
where, TP denotes the count of true positives, FP denotes the count of false positives, whereas TN denotes the count of true negatives and FN denotes the count of false negatives.
While,
We used two performance metrics namely sensitivity and specificity and they are calculated as follows:
A sensitivity percentage of 97.51% is the substantial result because it specifies the highest likelihood of true positive case results in patients with cardiac disease, which implies an accurate 97.11% cardiac disease diagnosis for a new case with undiagnosed cardiac disease in the hospital. A timely and precise prediction of cardiac disease is crucial for timely intervention and increase survival time and rate, this high sensitivity together with the comparatively high 0.8681 and 0.8977 AUC curves indicates a high precision in the cardiac diseases diagnosis in the developed deep learning model. The cardiac disease diagnostic specificity percentage is 95.1%.
The deep CNN model’s efficiency depends on the DNN classifier choice during the training phase. In this research, after the conclusion of the training phase, the weights and the biases of the deep learning prediction were tuned from the deep learning model. The dataset is partitioned into a training and a testing dataset, and the training set is utilized to form classifiers. While, the testing set verified the efficiency.
Dataset size  Our proposed DCNN %  ECNN [ 
CNN [ 
DNN [ 
Deep^{2} [ 

10,000  84.5  74.7  74.2  75.9  74.7 
20,000  87.8  75.4  75.3  77.4  76.1 
30,000  90.4  76.7  76.3  79.7  78.7 
40,000  92.4  82.4  84.8  82.3  83.4 
50,000  93.7  83.2  85.4  84.5  84.5 
60,000  95.6  84.3  86.7  86.7  87.8 
70,000  97.11  89.3  88.8  88.6  90.4 
Method  Execution Time (s) 

Our proposed model  
ECNN [ 

CNN [ 

DNN [ 

Deep^{3} [ 
This paper proposed a Deep learning CNN model for heart disease Classification. The heart disease detection performance with 96 percent confidence using accuracy and specificity metrics. The proposed deep learning classification neural model has a deep learning mode for prediction with nonlinear regularized learning. The proposed model can, therefore, perform with high accuracy and reliability. and can reduce the miss ratio. The model can aid patients and medical professionals globally to help the public in developing regions with few medical personnel for heart disease. We employed feature extraction to enhance the accuracy of the proposed technique. The feature extraction technique has added to the precision of the deep learning model. The model performance is verified with a full and minimum set of features. Less features enhance the processing time of the classification process while the accuracy is compromised. The performance of classifiers with less features has been analyzed with experimental results. Experimental results demonstrate that flexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.
Though, the limitation of this study is the lack of investigating the bandwidth performance for the internet of things using our platform which would be very crucial for future study.
We would like to thank for funding our project: Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R113), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.