Deep learning architectures for multi-label classification of intelligent health risk prediction
© The Author(s). 2017
Published: 28 December 2017
Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases.
Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable.
Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient’s risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging problem that warrants further studies.
Chronic diseases are responsible for the majority of healthcare costs worldwide [1, 2]. An early diagnosis from an expert can help save a patient in terms of healthcare costs and extend the lifespan and quality of life for a patient. Early diagnosis of a chronic disease is often difficult due to the complexity and variability of the factors that lead to the disease. In an effort to help physicians diagnose these types of diseases early, computational models are being utilized to predict if a patient shows signs of one or more types of chronic diseases. The advantage of modern big data analysis allows physicians to infer information from patient data with less computational time and cost. This will allow physicians to build powerful tools for the purposes of intelligent health risk prediction.
Recently, deep learning techniques are being used for all different purposes with great success and are becoming more popular within various disciplines. Because of its generality, similar architectures put together through deep learning can be applied to many classification problems. Particularly within the medical field they are increasingly being used as a tool for multi-label classification. For example, Mayr et al. use a Deep Neural Network as a way to identify different sets of chemical compounds for toxicity prediction for humans , Lipton et al. use Recurrent Neural Networks to analyze time-series clinical data to classify 128 different diagnoses , and Esteva et al. use Convolutional Neural Networks to identify skin-cancer .
In this study, hypertension, diabetes, and fatty liver are three chronic diseases that are analyzed to predict types of chronic diseases for a patient. The diagnosis that is given for a certain patient can be one of the three, some combination of the diseases, or can be diagnosed as showing no signs of any of the diseases. This means that overall there are eight different diagnoses that can be given.
The layout of the paper is as follows: Methods will describe the two Deep Learning architectures that were used as a predictor for the multi-label classification dataset, the different types of algorithms that serve as a benchmark for comparison purposes, and explain evaluation methods that show how Deep Learning architectures perform when compared against traditional and other similar multi-label classification type methods; Results will describe the data and report the differences of performance between the methods chosen; Finally, discussion and conclusions are made about the performance of deep learning architectures for the purposes of predicting chronic diseases in physical examination records.
Several different machine learning methods are brought together to compare the performance of Deep Learning architectures on the physical examination data. In this section, combinations of traditional machine learning methods are used, plus there are a few methods that were specifically developed to solve multi-label classification problems. The other traditional methods can be used to solve multi-label problems, but generally involves some manipulation of the dataset in order for the algorithm to interpret targets of a dataset correctly. In other words, it transforms a multi-label dataset into a single-label dataset with multiple classes. There are many different techniques that have been used to handle this type of conversion. There are generally two categories for multi-label classification problems: problem transformation or algorithm adaption methods. One of the more popular problem transformation techniques is called the Label Powerset (LP) , where each unique set of labels for a multi-label dataset is considered a single label. This unique set of labels is considered a powerset. A classifier is trained on these powersets in order to make a prediction. Some of the following methods make use of this particular technique in order to handle multi-label classification. However, there are some drawbacks when manipulating the data to suit this format. It is common for LP datasets to end up with a large amount of represented classes and few samples of each class to train on. An advantage that Deep Learning methods have over similar problem transformation techniques is that it can train on the original data without needing to resort to some type of conversion of the data. These Deep Learning methods fall more into the algorithm adaptation category.
There are a couple of methods that were used to compare against the Deep Learning techniques that make use of, or have a variation of, the LP transformation. In particular, the Random k-Labelsets (RAkEL) method for multi-label classification [7, 8] is one such method that utilizes LPs to train on groups of smaller, randomly selected sets of labels, which are of size k, using different classifiers on groups of LPs, then uses a majority voting rule as the basis for selecting target values. If the average of the predictions for a label is above a certain threshold, then the label is chosen as true for that instance.
The ELPPJD method  is an ensemble multi-label classification method that uses a technique similar to LP and RAkEL where the data is transformed into a multi-class problem, then performs a joint decomposition subset classifier method to handle imbalanced data. This joint decomposition creates subsets of the data based upon the number of samples per LPs.
The following section describes the classification methods that we used for prediction. Besides the Deep Learning methods, most of these classifiers were part of a single label, multiclass step when used with RAkEL and MLPJTC after the dataset transformation. These classifiers were the “base” classifiers for the previous mentioned multi-label classification methods.
This process of a forward pass and backpropagation continues until a certain number of iterations are met, or the network converges on an answer. Another way to look at the method is that the architecture is using the data to find a mathematical model or function to best describe the data. As the network is trying to learn, it is constantly searching for a global minimum value such that predictions can be accurate.
The C4.5 algorithm  is a classification method that is used to build a decision tree. It uses the concept of information gain and attributes of the data to split nodes of a tree into one class or another. It decides the best attribute of the data to properly split samples of the data and follows some base cases to add more nodes to the tree.
Support Vector Machines (SVM) work by trying to separate the classes from samples of a data into different hyperplanes. It tries to maximize the distance between classes as much as possible. It can use one hyperplane for linear classification, or it can have an infinite number of hyperplanes for nonlinear classification. The way that this is achieved is utilizing kernel functions that have the ability to linearly separate the data.
For this study, there were two different implementations of SVM algorithms that were tested with the physical examination dataset. One implementation used Sequential Minimal Optimization (SMO)  while the other is a slight variation of the SMO algorithm that was developed from the library package LibSVM [14, 15].
Random Forest is another decision tree type algorithm that takes advantage of the concept of bagging, or using many different learned models together to make an accurate prediction . It creates a collection of different decision trees based on random subsets of samples per tree and decides which class to predict by employing a voting mechanism to rank the decisions.
ML-KNN is an extension of the k nearest neighbors algorithm for multi-label classification . It works by determining the k nearest neighbors for an instance as it is passed to the algorithm, then the information gained from the labels that are determined to be mostly associated with the instance is used to predict the appropriate LP for the unseen instance. BP-MLL is multi-label neural networks algorithm that can be considered for performance comparison, which will be included in our future work. This algorithm was successfully applied to classification of functional genomics and text categorization .
Deep learning architectures
Deep Learning architectures are becoming more popular as a set of tools for machine learning. For multi-label classification, these types of systems are performing very well, even sometimes outperforming humans in certain aspects. Here, Deep Learning methods are used to predict chronic diseases for intelligent health risk prediction. What follows is a brief description of the types of architectures that we implemented when using physical examination records to predict chronic diseases. There are two different implementations of the DNN used for multi-label classification: one for problem transformation, and another for algorithm adaptation.
Deep Neural Networks (DNN) are an extension of the MLP and is usually considered a DNN if the MLP has multiple hidden layers [19, 20]. In addition to multiple layers, there are different types of activation functions and gradient descent optimizers that help to achieve a solution to an issue that MLPs suffer from which is the vanishing gradient problem. The vanishing gradient problem arises whenever a network is trying to learn a model, but the gradients of an error are so small that adjustments to the weights through backpropagation almost make no difference to the learning process and gets to a point of never reaching a global minimum. As mentioned before, there are different activation functions that are typically used for MLPs and DNNs, such as sigmoid or hyperbolic tangent functions. However, specifically for Deep Learning, different activation functions have been proven to achieve better results in certain cases. One of these activation functions is called a Rectified Linear Unit (ReLU). For some activation functions, the evaluation of a node can lay between negative one and positive one. However, for the ReLU function, an evaluation that is below zero is cut off and the value can only be between zero and one, or more formally f(x) = max(0, x) where x is the result of the equation coming from the node of the network. Gradient descent optimizers are optimization algorithms used for the purposes of finding a local minimum. Hyper parameters such as learning rates and momentum serve these gradient descent algorithms by shifting how much to move through a function space in order to converge on a global minimum. If a value is either too low or too high then the optimizer may miss the global minimum entirely and focus on a local minimum, or perhaps it may never converge at all.
To optimize the hyper parameters of these deep learning networks we opted to go with a grid search to find the best solution and let the networks converge on a model that suits the data. A grid search is one in which there are multiple different variables one should account for in a deep learning model to reach the global minimum as fast or as accurate as possible. For the multilayer perceptron, there were three different parameters: epochs, learning rate, and hidden layers. In practice, these are the parameters that changed prediction results the most. Epochs are how many iterations of the data the network will be used to train a model, the learning rate is how fast or slow the gradient decent optimizer adjusts to reach the minimum, and hidden layers refer to the number of individual layers between the input and output layers. The DNNs in our example are fully connected networks, meaning that each node contains a connecting edge to all of the nodes in the successive layer in the network. Hidden layer units are the number of nodes that exist in each individual hidden layer in the network. The number of units that were chosen came to be 35. This is based on one of the parameters that WEKA uses for their multi-layer perceptron, where they use the equation a = (attributes + classes)/2 to determine some number of units for a layer.
There are also some different activation functions that were used, either the sigmoid function or ReLU, and dropout layers were also chosen. Dropout was developed for the purposes of helping a network avoid overfitting . The basic idea behind dropout is to block certain nodes from firing in the network and allow other nodes the opportunity to learn through different connections or infer different information by only allowing access to certain information. There are differing opinions on whether or not one should allow dropout between each layer, or only during the last hidden layer and output. In this study both options are investigated to get an overall view of how the network performs.
In order to compare these different methods, accuracy cannot be the single metric used to determine the effectiveness of an algorithm. There are multiple other methods that typically get used to get an overall census on how a method performs. For example, one method could have a very high accuracy, but the data could be imbalanced and the model could be biased towards some certain class that dominates the dataset and only selects that class as the prediction based on the training data, ensuring that most of the guesses are labeled correct even though it is simply selecting the dominating class most of the time without actually learning any information about the data.
Classifier evaluation platform and development environment
The results of the classifiers for single-label, multi-class dataset
The results of the classifiers for multi-label dataset
Results and discussion
Dataset and preprocessing
The physical examination dataset is from a medical center where 110,300 anonymous medical examination records were obtained . In the table of dataset, each row represents the physical examination record of a patient and each column refers to a physical examination item or feature, except for the last six columns that indicate disease types. The dataset includes 6 normal chronic diseases including hypertension, diabetes, fatty liver, cholecystitis, heart disease, and obesity and the prediction in this study focuses on the first three of them. Each type of six diseases corresponds to a class label in the classification. From over 100 examination items, 62 features were selected as significant based on expert knowledge and related literature. These items are 4 basic physical examination items, 26 blood routine items, 12 urine routine items, and 20 items from liver function tests. One may get more details about the dataset from  and website provided at the end of this paper.
In order to get some evaluations on the data, a ten-fold cross validation step is performed on the data, where 90% of the data is used for training and 10% is left for testing. Usually, random sampling is enough to get results from cross validation, however with the physical examination records another approach is needed because not all classes were being represented in the training for the model of the classifier.
Comparison of different classifiers
In Table 1, various popular classification methods are compared against each other to analyze the performance of the single-label, multi-class dataset. LibSVM and SMO are different types of support vector machines, MLP is the WEKA implementation of the Multilayer Perceptron, J48 is the Java implementation of the C4.5 decision tree algorithm, DNN represents the deep learning architecture that was implemented in TensorFlow, and RF is the Random Forest classifier.
The support vector machines were not able to handle the data as well as the decision tree type algorithms, which scored the best overall. MLP and DNN similarly scored lower than the decision tree algorithms. In the case of single label, multi-class, a bagging type algorithm does fairly well on this dataset.
For Table 2, the classifiers from Table 1 are used as a base classifier for the RAkEL method in order to handle multi-label classification. The difference here is in the MLkNN and DNN methods. These two methods could handle the data without first transforming it into a LP. In all cases of RAkEL except for SMO, the results were improved from the previous table. MLkNN performed the worst out of all methods. DNN had the best accuracy, but when considering the other metrics listed in the table, RAkEL with Random Forest as a base classifier was the best performing classifier overall. This makes sense, because not only is RAkEL creating random subsets of the data, but Random Forest is also generating subsets of the samples for its decision trees. This allows for a very large coverage of all the features to be able to strongly identify correlations in the data. These subsets could allow for more precision when making a prediction. The DNN architecture is trying to find correlations from the data as a whole without any type of ranking, voting, or making subsets of the samples, so there is a wider net of interpretation from the dataset. Also, different adjustments of hyper-parameters could help increase precision and recall values. This dataset in particular has a large amount of TN values which dominate the terms in the equation for accuracy. The model itself tended toward a negative prediction. This is one reason why accuracy was so high while other metrics were lower.
Optimization of deep learning parameters in single-label data
A grid search of hyper parameters was used when trying to find the optimal parameter to use with the physical examination dataset. When using a grid search one could randomly choose a set of parameters and train using the chosen set, then repeat until a certain number of runs were achieved, or another option would be to iterate through all possible combinations to get performance metrics for each run. The latter was chosen as the preferred method of evaluation in addition to the ten-fold cross validation step. The epochs, or iterations were 775 and 1000, the learning rate was 0.01, 0.05, 0.75, and 0.1. Hidden layers for the single label data were set as either 1 or 2. The Sigmoid and ReLU activation functions were also used for comparisons to evaluate how each of them compared.
The structure of the DNN here is very similar to the implementation of the MLP provided by the WEKA software benchmark tool. However, there are some differences which accounts for the variation in the results. In terms of nodes in the network, each represented node in the WEKA version uses the sigmoid activation function including the output layer. For the loss function, the squared-error loss is used with backpropagation for learning. In the case of the TensorFlow implementation, the output layer of the network was made up of linear units that were not squashed by any activation function. For the loss function, a softmax function with cross entropy was used to calculate the error across the network, then it is passed to an optimizer that implements the Adam algorithm  for stochastic gradient optimization.
Impact of deep learning parameters for multi-label data
DNN results for multi-label data with respect to different number of units
Units Per Layer
The specific architectures that were developed for the physical examination data were DNNs. However, there are a variety of different architectures that could have been chosen. In this case, it seemed that other architectures did not perform as well as DNNs, possibly due to the fact that the data itself is not so complex as to need the level of computation that other architectures like Convolutional Neural Networks or Recurrent Neural Networks would need. In addition to the complexity, the learning method of the data generally would fit a regression type of model to learn against the data, which does not necessarily fit the type of data that is generally associated with the other architectures. In most cases, such a type of classification of this data falls in the category of DNNs.
For the multi-label dataset, an increase in accuracy could be explained by the fact that each class has more training samples since the classes are not mutually exclusive. Considering the distribution of each LP in Fig. 1, the imbalanced data is less of an issue and each class is more likely to have some representation when random sampling for the training set. Some adjustment could be made to the threshold value when the prediction of the output layer is calculated, which could also improve the accuracy of the model.
The introduction of batch normalization has also improved the results of the training . Batch normalization is the process in which mini-batches of the training data are used to step through the network instead of processing the entire training dataset as one step of training. The reason is to minimize the impact of the covariate shifts from the features of the input data, effectively normalizing the layers and reducing the need for other architecture regularization techniques such as dropout layers. Another advantage is that batch normalization can reduce the amount of epochs needed to train the network. For example, before batch normalization, our network achieved an accuracy of 89.90%, after 1000 epochs. After batch normalization using a batch size of 512, the accuracy increased to 92.07%, with only 100 epochs, significantly reducing the amount of training time.
Some architectures can be sensitive to initialization weights. Although the purpose of a Neural Network is to be able to adjust weights even from random initial values, setting the initial weights can significantly affect the results of the prediction depending on the architecture. In the described implementation, a truncated normal is used to initialize the weights within two standard deviations from the mean. The standard deviation was selected to be 0.001 with a mean of zero, so the random values ranged between 0 and 0.003. Previously implemented architectures used a randomized normal distribution for values ranging between zero and one, but selecting a truncated normal so close to zero increased all evaluation measures by a few points. This architecture seemed to learn fairly well no matter the initialization values. Evaluation measures varied only a small amount.
In this study, a multi-label classification method is developed using deep learning architectures for the purposes of predicting chronic diseases such as hypertension in patients for physicians. Such architectures are valuable tools as they are able to calculate correlations in the data through iterative optimization techniques. The results show that DNNs give the highest accuracy among all six popular classifiers. The F-score of DNNs is slightly lower (but compatible) than Random Forrest and MLP classifiers and but much higher than that of SVM and MLKNN classifiers. DNNs play a valuable role in the future of multi-label classification methods because they are able to adapt to the original data and can eventually find a decent optimized function even with rudimentary pieces from which to learn information. Some expert knowledge could vastly improve the rate and ease at which a network could learn the intricate details of a system. In this case, there are some areas of improvement that could be made in terms of the architecture and a thorough investigation of the way the data is passed through the architecture of the network should be considered. Further modification of this architecture could enhance the performance of the model in order to achieve better results for precision, recall, and f-score values. Deep learning architectures provide a powerful way to model complex correlations of features together to form an optimized function from which physicians can predict chronic diseases. Additional improvements to the model could easily allow for the inclusion of other chronic diseases as newer data is gathered.
We thank the Collaborative Innovation Center on Internet Healthcare and Health Service of Henan Province, Zhengzhou University for providing medical records for analysis in this study.
The work was partially supported by the USA DOD MD5i-USM-1704-001 grant and by the Frontier and Key Technology Innovation Special Grant of Guangdong Province, China (No. 2014B010118005). The publication cost of this article was funded by the DOD grant.
Availability of data and materials
The physical examination dataset used in this study is located at http://pinfish.cs.usm.edu/dnn/. There are two versions of the data available for download: a simple text file and an ARFF file for use with WEKA. Details about the format of the data are located on the webpage. The personally identifiable information from this dataset has been removed to ensure patient anonymity.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 18 Supplement 14, 2017: Proceedings of the 14th Annual MCBIOS conference. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-18-supplement-14.
CZ, and PG conceived the project. AM implemented the deep learning architectures and performed the analysis with other classifiers. RL developed the MLPTJC method that was used for comparisons of different classifiers for the single-label and multiclass classifiers. AM and CZ analyzed the results and wrote the paper. HW, AO and ZZ participated in the development of deep learning methods. BY, ZZ and HH provided advice and suggestions for the experiment and proofread the document. All authors have read and approved final manuscript.
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