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An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction

This study proposes an efficient neural network with convolutional layers to classify significantly class-imbalanced clinical data. The data are curated from the National Health and Nutritional Examination Survey (NHANES) with the goal of predicting the occurrence of Coronary Heart Disease (CHD). While the majority of the existing machine learning models that have been used on this class of data are vulnerable to class imbalance even after the adjustment of class-specific weights, our simple two-layer CNN exhibits resilience to the imbalance with fair harmony in class-specific performance. In order to obtain significant improvement in classification accuracy under supervised learning settings, it is a common practice to train a neural network architecture with a massive data and thereafter, test the resulting network on a comparatively smaller amount of data. However, given a highly imbalanced dataset, it is often challenging to achieve a high class 1 (true CHD prediction rate) accuracy as the testing data size increases. We adopt a two-step approach: first, we employ least absolute shrinkage and selection operator (LASSO) based feature weight assessment followed by majority-voting based identification of important features. Next, the important features are homogenized by using a fully connected layer, a crucial step before passing the output of the layer to successive convolutional stages. We also propose a training routine per epoch, akin to a simulated annealing process, to boost the classification accuracy. Despite a 35:1 (Non-CHD:CHD) ratio in the NHANES dataset, the investigation confirms that our proposed CNN architecture has the classification power of 77% to correctly classify the presence of CHD and 81.8% the absence of CHD cases on a testing data, which is 85.70% of the total dataset. This result signifies that the proposed architecture can be generalized to other studies in healthcare with a similar order of features and imbalances. While the recall values obtained from other machine learning methods such as SVM and random forest are comparable to that of our proposed CNN model, our model predicts the negative (Non-CHD) cases with higher accuracy. The balanced accuracy of our model (79.5%) is also better than individual accuracies of SVM or random forest classifiers. The CNN classifier results in high specificity and test accuracy along with high values of recall and area under the curve (AUC).


Introduction
Heart disease is a leading cause of death today, with coronary heart disease (CHD) being the most common form of cardiovascular disease that accounts for approximately 13% of deaths in the US (Benjamin, 2019). Timely diagnosis of heart disease is crucial in reducing health risk and preventing cardiac arrests. An American Heart Association study projects an almost 100% increase in CHD treatment by 2030 (Benjamin, 2019). Major risk factors such as smoking, hypertension, hyper cholesterol and diabetes have been studied in connection to CHD (Kannel, et al., 1971;Stamler, et al., 1993;Vasan, et al., 2001;Kannel, 1996;Burke et al., 1997;Celermajer et al., 1993;Chobanian et al., 2003;Haskell et al., 1994;Zeiher et al., 1993;Ahmed et al., 2017). Ahmed. et al. (2017) show that Body Mass Index (BMI) and systolic blood pressure are the two most critical factors affecting hypertension. Fava. et al. (2013) conclude significant association between age, sex, BMI and heart rate with hypertension. Studies in general population indicate that high level of creatinine in blood can increase the risk of CHD (Irie et al., 2006;Wannamethee, Shaper & Perry 1997). Additionally, blood cholesterol and glycohaemoglobin levels are found to be persistently and significantly high in patients with CHD (Burchfiel, 1997;Meigs et al., 1997).
Several researchers have used statistical and machine learning models on echocardiography images (Nakanishi et. al., 2018;Madani et. al., 2018) and electrocardiography signals (Jin, 2009;Shen et. al., 2016) to predict clinically significant parameters related to CHD in patients, such as heart rate and axis deviation. Boosted algorithms such as gradient boost and logit boost have been used in literature to predict FFR and cardiovascular events (Weng et. al, 2017;Goldstein, 2017). Frizzell et al. and Mortazavi et al. built prediction models to determine the presence of cardiovascular disease using the 30-day readmission electronic data for patients with heart failure.
The C-statistic of the models reported varied from 0.533 to 0.628, showing an improvement with the machine learning approach over traditional statistical methods.
Numerous risk factor variables often make the prediction of CHD difficult, which in turn, increases the cost of diagnosis and treatment. In order to resolve the complexities and cost of diagnosis, advanced machine learning models are being widely used by researchers to predict CHD from clinical data of patients. Kurt et al. (2008) compared prediction performances of a number of machine learning models including the multilayer perceptron (MLP) and radial basis function (RBF) to predict the presence of CHD in 1245 subjects (Kurt et al., 2008). The MLP was found to be the most efficient method, yielding an area under the receiver operating characteristic (ROC) curve of 0.78. Shilaskar and Ghatol (2013) proposed a hybrid forward selection technique wherein they were able to select smaller subsets and increase the accuracy of the presence of cardiovascular disease with reduced number of attributes. Several other groups have reported techniques, such as artificial neural network (ANN), fuzzy logic (FL) and deep learning (DL) methods to improve heart disease diagnosis (Uyar, 2017;Venkatesh, 2017). Nonetheless, in most of the previous studies, the patient cohort was limited to a few thousands with limited risk factors.
We propose an efficient neural network with convolutional layers using the National Health and Nutritional Examination Survey (NHANES) dataset to predict the occurrence of CHD. A complete set of clinical, laboratory and examination data are used in the analysis along with a feature selection technique by LASSO regression. Data preprocessing is performed using LASSO followed by a feature voting and elimination technique. The performance of the network is compared to several existing traditional ML models in conjunction with the identification of a set of important features for CHD. Our architecture is simple in design, elegant in concept, sophisticated in training schedule, effective in outcome with far-reaching applicability in problems with unbalanced datasets. Our research contributes to the existing studies mainly in three ways: 1) our model uses a variable elimination technique using LASSO and feature voting as preprocessing steps; 2) then we leverage a shallow neural network with convolutional layers, which improves CHD prediction rates compared to existing models with comparable subjects. The 'shallowness' is dictated by the scarcity of class-specific data to prevent overfitting of the network during training; 3) in conjunction with the architecture, we propose a simulated annealing-like training schedule that is shown to minimize the generalization error between train and test losses.
The rest of the paper is organized as follows: Section 2 explains data preparation and the preprocessing techniques. In section 3, we illustrate the convolutional neural network architecture with details on the training and testing methodology. In section 4 we demonstrate the results obtained from our model with performance evaluation metrics and compare it with existing models. Section 5 is the conclusion and discussion section. Here, several extensions to the research are proposed.

Data Preprocessing
Our study uses the National Health and Nutrition Examination Survey (NHANES) data from 1999-2000 to 2015-2016. The dataset is compiled by combining the demographic, examination, laboratory and questionnaire data of 37,079 (CHD -1300, Non-CHD -35,779) individuals as shown in Figure 1. Demographic variables include age and gender of the survey participants at the time of screening. Participant weight, height, blood pressure and body mass index (BMI) from the examination data are also considered as a set of risk factor variables to study their effect on cardiovascular diseases. NHANES collects laboratory and survey data from  participants once in every two years depending on their age and gender. In addition, based on the already existing validated experimental research, a comprehensive list of risk factor variables is selected from the laboratory tests conducted. Questionnaire data comprises of questions asked at home by interviewers using a Computer-Assisted Personal Interview (CAPI) system as mentioned in the NHANES website (NHANES, 2015). A total of 5 dichotomous predictor categorical variables are selected from the questionnaire data which have been shown to affect CHD (references, required). In all, 30 continuous and 6 categorical independent variables are used to predict the likelihood of coronary heart disease. For this study, coronary heart disease (CHD) is used as the dichotomous dependent variable. Awareness of CHD is defined as "yes" response to the question "Have you been ever told you had coronary heart disease?" Table I shows the categorical independent and dependent variables in the dataset considered for model development. The exhaustive list of variables is: gender, age, annual-family-income, ratio-family-incomepoverty, 60sec pulse rate, systolic, diastolic, weight, height, body mass index, white blood cells, lymphocyte, monocyte, eosinophils, basophils, red blood cells, hemoglobin, mean cell volume, mean concentration of hemoglobin, platelet count, mean volume of platelets, neutrophils, hematocrit, red blood cell width, albumin, alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), cholesterol, creatinine, glucose, gamma-glutamyl transferase (GGT), cholesterol, creatinine, glucose, iron, iron, lactate dehydrogenase (LDH), phosphorus, bilirubin, protein, uric acid, triglycerides, total cholesterol, high-density lipoprotein (HDL), glycohemoglobin, vigorous-work, moderate-work, health-Insurance, diabetes, blood related diabetes, and blood related stroke. However, in this list of variables, there are a couple of linearly dependent variables in terms of their nature of acquisition or quantification and some uncorrelated variables (annual family income, height, ratio of family income-poverty, 60 sec pulse rate, health insurance, lymphocyte, monocyte, eosinophils, total cholesterol, mean cell volume, mean concentration of hemoglobin, hematocrit, segmented neutrophils). We do not consider these variables for subsequent processing and analysis.

LASSO Shrinkage and Majority Voting
LASSO or least absolute shrinkage and selection operator is a regression technique for variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model it produces. In LASSO, data values are shrunk toward a central point, and this algorithm helps in variable selection and parameter elimination. This type of regression is wellsuited for models with high multicollinearity. LASSO regression adds a penalty equal to the absolute value of the magnitude of coefficients, and some coefficients can become zero and are eventually eliminated from the model. This results in variable elimination and hence models with fewer coefficients. LASSO solutions are quadratic problems and the goal of the algorithm is to minimize: which is the same as minimizing the sum of squares with constraint ∑ | | ≤ . Some of the values are shrunk to exactly zero, resulting in a regression model that's easier to interpret. A tuning parameter, λ which is the amount of shrinkage, controls the strength of the regularization penalty.
When λ = 0, no parameters are eliminated. The estimate is equal to the one found with linear regression. As λ increases, more coefficients are set to zero and eliminated. As λ increases, bias increases and as λ decreases, variance increases. The model intercept is usually left unchanged.
The value for a variable (factor) can be interpreted as the importance of the variable in terms of how the it contributes to the underlying variation in the data.

Convolutional Neural Network (CNN)
The challenge of predicting the existence of CHD in patients refers to the task of binary classification. Under certain constraints, neural network (LeCun, 2015; Goodfellow, 2016) has been proven to be an effective parametric classifier under supervised settings. Recently, with the explosion of structured data, deep neural networks incorporating a large number of applicationspecific hidden layers, have demonstrated significant improvement in several areas including speech processing, applications involving image processing, and time series prediction (LeCun, 1995). There is a vast body of deep learning architectures that are fine-tuned and rigorously trained using big datasets. An artificial neural network (Iandola, 2016;Krizhevsky, 2012;Szegedy, 2017;He, 2016) successively transforms the input data over sequential hidden layers and estimates the error at the output layer. The error is back-propagated to iteratively update the layer weights using Among various algorithms that attempted to overcome this problem, data augmentation (Krizhevsky, 2012;Radford, 2015) is a widely used technique that artificially generates examples to populate small datasets. However, such a procedure is biologically implausible in most clinical datasets. For example, augmented measurements of a CHD phenotype, such as platelet count, might not correspond to possible readings of a subject. It is because the underlying principles of the statistical generation and the biological sources of platelet count readings may be fundamentally different. Poor training due to small or imbalanced datasets and susceptibility to adversarial examples lead to poor and unreasonable classification. Unlike many computer vision tasks, such as semantic labeling, chat-bot configuration, and hallucinogenic image synthesis (Mordvintsev, 2015), erroneous prediction in medical research is accompanied by a significant penalty.
For example, faulty prediction of a subject having chronic CHD may leave the subject untreated or misdirect the possible therapeutic medication. Therefore, one of the prime objectives of this paper is to improve classification accuracy, i.e. the prediction accuracy of the subjects with and without the presence of CHD. There are several other relevant concerns related to misclassification in medical research (Marcus, 2018). To overcome these limitations and driven by the success of deep networks, we propose a shallow convolutional neural network, where the convolution layers are 'sandwiched' between two fully connected layers as shown in Fig. 2.

CNN Architecture
The architecture is a sequential one-input-one-output feedforward network. For simplicity, we assume the class of subjects with presence of CHD as class '1' and the subjects with absence of CHD as class '0'. As mentioned in the previous section, the number of active phenotypes of CHD obtained from majority voting is 50. Let the number of training examples be N, which indicates that the input layer in Fig. 2  probability is performed to reduce overfitting. Following the fully-connected layer, there is a cascade of convolution layers. In the first convolution layer, there are two filters of kernel width 3 and stride 1. The layer is not provided with external zero-padding. In the pooling layer, we rigorously experiment with different pooling strategies and find average pooling working marginally better than max pooling under all constraints. The first convolution layer converts the output of fully connected block ∈ ℛ 64 to a tensor of dimension ℛ 64 1 . The tensor is then subjected to batch normalization, nonlinear transformation and average pooling with an output tensor of dimension ℛ 31 2 .
The filters in the last no-zero-padded convolution layers are taken with kernel 5 and stride 1, delivering an output tensor of ℛ 13 4 to the next dense layer after the average pooling layers.
The categorical output is observed at the end of the softmax layer, where we set the loss function as the categorical cross-entropy loss. The bias in each layer is initialized with random numbers drawn from a truncated normal distribution with variance 1 √ , where is the number of 'fan-in' connections to the layer. We use Adam optimizer with learning rate 0.005, 1 = 0.9, 2 = 0.999 and zero decay. Our proposed architecture consists of 32,642 trainable and 1,164 non-trainable parameters. We experiment with several hyperparameters that are associated with our model to obtain consistent class-wise accuracy. We provide results by varying subsampling of input data, epochs, class-weights, the number of neurons in each dense layer except the last one, and the number of filters in each convolution layer during training.

Training schedule
During training, the class weight ratio, which is adjusted as a penalty factor due to class imbalance, is defined as the ratio of CHD and Non-CHD dataset. For example, a class weight ratio of 10:1 indicates that any misclassification of a CHD training sample will be penalized 10 times more than a misclassified Non-CHD sample during the error calculation at the output prior to backpropagation after each epoch. Although, we use dropout layers in our CNN model, we also use this training schedule in order to further reduce possible overfitting. The intuition is to initially train the model with 1:Ν weight ratio for sufficiently large number of epochs and then, gradually increase the weight ratio with a steady decline in the number of epochs. Let the actual class weight ratio is 0 : 1, which we take as a factor 0 .

Competitive Approaches
Machine learning classification methods have shown to potentially improve prediction outcomes in coronary heart disease. Such classification methods include logistic regression, support vector machines, random forests, boosting methods and multilayer perceptron (Goldstein, 2017). Logistic regression models the prediction of a binomial outcome with one or more explanatory variables, using a standard logistic function which measures the relationship between the categorical dependent variable and one or more independent variables by estimating the probabilities. The logistic function is given by, ( ) = 1 1+ − which in common practice is known as the sigmoid curve. Support vector machine (SVM) is a binary classification algorithm which generates a (N-1) dimensional hyperplane to separate points into two distinct classes in an N dimensional plane. The classification hyperplane is constructed in a high dimensional space that represents the largest separation, or margin, between the two classes.

Summary Statistics
For the purpose of variable selection in our classification model, we start out by investigating correlations among 30 continuous predictor variables. Correlation is found to be high (0.77) between serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) as given from Fig 3. However, AST is a major risk factor in the prediction of CHD, as has been reported in the literature (Shen et. al, 2015). Jianying (2015) finds AST levels to be significantly higher in CHD patients than in the control group and hence can be used as biochemical markers to predict the severity of CHD. A high correlation of 0.89 was determined between body-mass-index and weight which seemed normal whereas correlation between hemoglobin and red blood cells was 0.74. While the association of hemoglobin with clinically recognized CHD is limited in research (Chonchol, 2008), the role of red blood cells in CHD is well researched in the literature (Madjid, 2013). It has been investigated that high blood glucose levels for non-diabetic patients can significantly increase the risk for development of CHD (Neilson, 2006) which is depicted in Fig. 3 by a high correlation value of 0.79 between glycohemoglobin and glucose. Further, we find a correlation of 0.46 between protein and albumin. Lower levels of serum albumin have been reported to be linked with increased levels of cardiovascular mortality as well as CHD (Shaper, 2004) while higher level of protein is reported to increase risks of CHD (Clifton, 2011). Serum Lactate dehydrogenase (LDH) is found to be correlated with AST (correlation coefficient of 0.41), consistent with previous studies which suggest that increased value of LDH in active population is associated with low risk of CHD (Kopel et. al., 2012). Due to the importance of the risk factors (as reported in existing literature) of some of the correlated variables and their association with CHD, LASSO regression was performed to correctly determine the predictor variables for further analysis.

Model results
To identify variables that contribute to the variation in data, we apply LASSO to 100 instances of randomly sampled datasets, with each containing 1300 examples of class CHD (negative class) and 1300 of class no-CHD (positive class). We set in eq. (2) as 6 and find that  To achieve the optimal number of features for training our CNN architecture, the threshold of the feature voting was kept in the range of 2 to 8. The highest accuracy obtained from training the network is 83.17% with a training loss of 0.489 with a threshold feature of 6 as shown in Fig. 4.
The corresponding highest test accuracy obtained is 82.32%. LASSO regularization reduces the coefficients of three of the continuous predictor variables (Body-mass-index, glucose and ALT) and one categorical variable to zero which is determined to be highly correlated as seen in section 4.1. With a threshold value of 6, the CNN architecture is trained separately on different sets of subsampled data sets. Subsampling is performed in varying ratios starting with the range 1300:13000 (CHD: Non-CHD) and increased to 1300:4000 as shown in Table II.  The corresponding test accuracy is reported as 82.32%.
In each subsampled set, all CHD subjects are taken into consideration and the Non-CHD subjects are randomly chosen without replacement. For training purposes, the neural network is trained by varying the misclassification penalty from 10:1 (CHD: Non-CHD) to 3:1 as shown in Table II.
The maximum accuracy of 83.51% and minimum training loss of 0.489 is obtained while training with a sampling ratio of 1300:4000 (CHD: Non-CHD) and misclassification penalty of 3:1(CHD: Non-CHD). The trained network is tested on a cohort of 31,779 (85.70% of the whole dataset) remaining samples and a test accuracy of 82.32% is obtained subsequently as reported in table II.
The optimal sampling ratio (1300:4000) of class 1 to class 0 is maintained for the final training of the network as illustrated in Table III.  accuracies becomes a minimum, indicating a reasonable generalization error as shown in Fig. 5.
During training the number of epochs, the number of neurons in each dense layer except the last one, and the number of filters in each convolution layer are varied to obtain the best fit of the model. We have fine-tuned several hyperparameters that are associated with our model to obtain consistent class-wise accuracy. The optimization is performed using Adam as the activation function with a learning rate 0.006 and 60 epochs, and no scheduling of learning rate is used during the training. The best test accuracy obtained is 82.32% where the penalty of misclassification of class 1 is set three times higher than that of class 0.  The performance of the proposed CNN classifier can be evaluated from the confusion matrix in Table IV. We specify the classification parameters as follows: TP: true positive classification cases (true predictions for class 1, i.e., true CHD predictions), TN: true negative classification cases (true predictions for class 0, i.e., true non-CHD predictions), FN: false negative classification cases (false predictions for class 1, i.e., false CHD predictions), FP: false positive classification cases (false predictions for class 0, i.e., false non-CHD predictions).
Some commonly applied performance rates calculated are the true positive rate (TPR), the accuracy of predicting CHD (class 1) and the true negative rate (TNR), the accuracy of predicting non-CHD (class 0). The detailed values of TPR, TNR, train accuracy, test accuracy and training loss for all class weights are given in Table III. In the present study, it is our objective for our classifier to predict the presence of CHD with higher (improved) accuracy than in previous studies. The recall rate (sensitivity) for correctly predicting the true positive rate for class 1 (presence of CHD) is 77% while the class 0 (absence of CHD) is 81%. The CNN classifier has been tested on 31,779 subjects while maintaining almost the same TPR and TNR, contrary to previous reported studies which have considered significantly smaller samples.
Partitioning a highly imbalanced dataset poses a lot of challenges and incurs unavoidable biases on a classifier's performance. While, it is advised to keep same ratio of class-specific data in training and testing in state of the arts, however, in a highly imbalanced dataset it is often very challenging to get a high class 1 (true CHD prediction rate) accuracy as the testing data size increases. The present method confirms that our proposed CNN architecture has the classification power of 77% to correctly classify the presence of CHD cases on a testing data, which is 85.70% of the total dataset. This result signifies that the proposed architecture can be generalized to other studies in healthcare with a similar order of features and imbalances.
The performance of our binary classifier is calculated by computing the ROC curve 2017). The area under the curve (AUC) value in the ROC curve is the probability that our proposed CNN classifier ranks a randomly chosen positive case (CHD) higher than a randomly chosen negative case (Non-CHD) (Tom 2005). Thus, the ROC curve behaves as a tool to select the possible optimal models and to reject the suboptimal ones, independently from the class distribution. It does so by plotting parametrically the true positive rate (TPR) vs the false positive rate (FPR) at various threshold settings as shown in Fig. 4. (Right). The calculated AUC is 0.767 or 76.7% which is comparable to previous studies related to CHD (Martinez, Schwarcz, Valdez & Diaz, 2018). In highly imbalanced data sets balanced accuracy is often considered to be a more accurate metric than normal accuracy itself.  These results confirm that the CNN classifier outperforms all existing commonly used machine learning models for coronary heart disease prediction in terms of accuracy in prediction for both CHD and Non-CHD classes.

Discussion, Conclusion and Perspectives
In this paper, we propose a multi-stage model to predict CHD using a highly imbalanced clinical data containing both qualitative and quantitative attributes. For such clinical data, imbalance is an unavoidable characteristic due to the nature of the availability of data. However, the data imbalance adversely affects the performance of any state-of-the-art classifier and, as a remedy of this problem, conventional techniques, such as data augmentation strategy, may be biologically implausible for several attributes in the data. By way of extensive experimentation and validation above, we establish that a special-purpose, shallow convolutional neural network exhibits a considerable degree of resilience towards data imbalance, thereby producing classification accuracy superior to the state-of-the-art machine learning models (Table 5). Our model is simple in concept, modular in design, resilient to data imbalance, and far-reaching in significance.
The model begins with the application of LASSO regression in order to identify the contribution of variables or attributes in the data variation. Using multiple instances of randomly subsampled datasets, LASSO is performed several times to check the consistency of the contribution, a crucial step of our algorithm to control the true-negatives of variable selection. Finally, a majority voting algorithm is applied to extract the variables of interest, a step that achieves dimensionality reduction by excising unimportant variables. We do not perform any conventional dimension reduction techniques, such as Local Linear Embedding, Principal Component Analysis and t-Stochastic Neighborhood Embedding, because these methods generally provide dimensions that are linear or nonlinear combination of the data variables, thus the methods are deficit in terms of interpretation of the derived dimensions. For example, a dimension as a linear combination of body mass index and alkaline phosphatase (ALP) is difficult to interpret. Now, whether or not, LASSO reflects the true importance of a variable through its shrinkage, and if it does not, then constructing an appropriate optimization function require further testing and would be a potential research avenue.
Once we obtain the contributory variables, we channel them to our 1D feedforward convolutional neural network. It is worth to mention that our work is not intended to provide a sophisticated architecture of a neural network. Instead, our goals are to establish the claim that one can apply convolutional stages despite the scarcity of data and the absence of well-defined data augmentation techniques; to show that the shallow layers of convolution indeed offer resilience to data imbalance (Table IV) problem by dint of a training schedule (section 3.2.1). We establish the claims in terms of our results in Table V, where, in contrast to Logistic Regression and Adaboost, our model provides a balanced classwise classification accuracy. In a cohort of 37,079 individuals with high imbalance between presence of CHD and Non-CHD, we show that it is possible to predict the CHD cases with 77.3% and Non-CHD cases with 81.8% accuracy, which indicates that the CHD class, which is deficit in the number of samples, does not suffer significantly from the data imbalance.
The preprocessing stage, consisting of repeated LASSO and majority voting, is pivotal in filtering out highly correlated variables, setting green flags only for the decorrelated ones to be channeled to our CNN model. This is clearly observed from Figure 3. Each LASSO stage maintains 1:1 ratio of CHD:Non-CHD data to avoid the adverse effect of data imbalance on the final shrinkage parameters . However, 1:1 ratio does not encapsulate enough variation of the Non-CHD as this class contains large number of data. We repeat LASSO with different randomly sampled subsets and eventually apply majority voting to assess the importance of a variable. Once the important/contributory variables are identified, we discard their LASSO values in this paper. In future work, instead of disregarding the LASSO values, we can integrate them to the subsequent CNN model as priors, a step that is expected to enhance the performance of the model.
A possible future extension of this work can be the implementation of CNN for predictions from similar clinical datasets with such imbalanced number of positive and negative classifications exist. As explained before, our model can also be used as a transfer learning model and the last two dense layers can be retrained with the new data. We will also focus on the characterization of the training data so that a sophisticated sampling technique can be designed instead of uniform sampling of input data. It would be interesting to test if the extension of shallow CNN models, in terms of architecture and data sampling, to implement neural net based learning on similar clinical data sets improves the prediction accuracy of the classification process.