The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified and classified at early stage itself in order to reduce health crisis. With this background, the current paper presents XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD precisely. The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, Whale Optimization Algorithm (WOA) with Deep Belief Network (DBN) model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA. In order to ensure a better ASD diagnostic outcome, a series of simulation process was conducted on ASD dataset.
The application of Artificial Intelligence (AI) methods is currently unstoppable and pervasive due to its incredible characteristics and high prevalence of adoption. But, it carries certain sorts of problems, opportunities, and risks which should be tackled in order to have an uncompromised and efficient development [
XAI system is capable of explaining the logic behind decisions, describe the strengths and weaknesses of decision making, and offer insights about the upcoming behaviours. AI applications are otherwise observed as autonomous driving systems and are utilized in financial, healthcare and military/legal sectors. In this case, there is a need to have reasoning behind the artificial partners in order to trust the decision and the data attained. The most common AI framework is now provided by DL approaches, whereas a Neural Network (NN) of tens or hundreds of layers of ‘neurons’, or ‘fundamental processing unit’, is employed. The complication of DL frameworks makes them act like ‘black boxes’ due to which it becomes nearly impossible to find the appropriate method for systems to provide certain answers [
The application of AI in healthcare, especially in diagnostic imaging, is rapidly increasing [
Autism Spectrum Disorder (ASD) is a mental illness development disorder that limits specific social behaviour and communication from normal evolution [
There have been several non-clinical and clinical diagnostic approaches available for ASD. Two established clinical diagnoses methods are Autism Diagnostic Interview (ADI) and Autism Diagnostic Observation Schedule-Revised (ADOS-R). Additionally, some of the approaches that are used in diagnostics are parent-based nonclinical or self-administered techniques namely, Social Communication Questionnaire (SCQ) and Autism Quotient Trait (AQ). It is important to note that the present mainstream ASD diagnostic tools consume significant time to conduct a comprehensive diagnoses. In order to develop the diagnostic procedure of ASD, scientists recently have begun to adopt ML approaches [
The current research article presents an XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD in a precise manner. The proposed XAI-ASD technique involves the designing of Bacterial Foraging Optimization (BFO) for Feature Selection (FS) approach. In addition, Whale Optimization Algorithm (WOA) with Deep Belief Network (DBN) model is applied for ASD classification process, in which the hyperparameters of DBN model are optimally tuned with the help of WOA. In order to validate the proposed method in terms of achieving better ASD diagnostic outcomes, a series of experimental analysis was conducted. The experimental results highlight that the results achieved by the proposed method were better and the proposed XAI-ASD technique is superior to other state-of-the-art techniques under different measures. In short, the contributions of the research paper are listed as follows.
A new XAI-ASD model is presented for detection and classification of ASD A new BFO-based FS technique is designed to choose an effective subset of features for ASD detection. A new WOA-DBN technique is derived for detection and classification of ASD. The performance of the proposed XAI-ASD technique was validated on benchmark dataset and the outcomes were examined under different evaluation parameters.
The research paper has the following sections. Section 2 provides a comprehensive review of existing ASD diagnosis techniques. Section 3 elaborates the proposed XAI-ASD technique and Section 4 validates the results attained from XAI-ASD technique. Finally, Section 5 draws the conclusion.
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In this study, a new XAI-ASD technique is designed and validated to detect and classify different stages of ASD. The presented XAI-ASD technique encompasses different processes namely, pre-processing, WOA-based FS, DBN-based classification, and WOA-based parameter tuning.
Initially, the patient data is pre-processed through three stages such as format conversion, missing value replacement, and class labeling. In the beginning, the input data in .arff format is modified into a companionable .csv format. Also, the missing values in the dataset are employed by median process. Eventually, class labeling model is incorporated to map the class labels of data against ASD.
After collecting and pre-processing the patient data to remove the undesirable data, FS procedure is executed in which BFO technique is utilized. A standard BFO approach has two important facts as given herewith.
Solution space initiation: the solution spatial dimension Bacterial initiation: the amount of bacteria is denoted by
Therefore, the fitness of
In
It has massive amounts of swimming and flipping actions [
But the swimming step length of
The bacteria is considered for both repulsion and attraction. The numeric relation is given herewith.
The bacteria repeats the reproduction process, when it is exposed to a better atmosphere; otherwise, it tend to die. So, based on chemotaxis and swarming methods, the fitness of whole bacteria is fixed and computed. The fitness of
Half of the bacteria are at better state while,
Then all the bacterium reproduce at the probabilities of
As demonstrated in
In FS issue, all the solutions are controlled towards the binary values of [0, 1]. In order to apply BFO technique in FS, BBFO technique is developed as a binary version. In BBFO technique, the solution is defined as 1D vector, but the length of vector depends on the amount of features in original dataset. All the cells in vector are valued at zero/one. The value ‘one’ represents the outcome feature that has been elected; otherwise, the value is defined as 0.
FS is modelled as a multi-objective optimization issue but two differing objectives are achieved i.e., less count of FS and superior classifier accuracy. Here, better result is the solution in terms of less number of FS and superior classifier accuracy. The classifier accuracy of KNN is employed as Fitness Function (FF) in order to assess the performance of entire search agents. In order to balance amongst the number of FS in all solutions (lesser) and the classifier accuracy (higher), the FF in
At last, the extracted features are then fed to DBN method for the classification of ASD. The framework of DBN method is shown in
It includes a classification model and stacked RBM in deep network. The training procedure of DBN includes supervised as well as unsupervised learning stages. The stacked RBM region is located in classification layer for the creation of DBN method. DBN training procedure starts at the unsupervised training of RBM, with the help of greedy algorithm and the result of previous RBM becomes the input of succeeding RBM. Finally, the supervised training procedure is performed in the classifier layer of DBN method.
In general, the main difference between DBN and MultiLayer Perceptron (MLP) layers is that the DBN layer is capable of performing the learning procedure through layerwise unsupervised training procedures. However, the MLP approach fails to perform feature learning tasks and is widely used for classification procedure. The resemblance between DBN and the MLP is that both are FC networks. Further, BP method is employed for supervised learning and is executed in the classifier layers of DBN. RBM is a likelihood-based graphical network, realized by the stochastic NN, in which two output states of the neuron are included. It depends upon BM approach whereas a FE method is considered based on energy model and optimization occurs through unsupervised training. Here, RBM method includes two sets of layers i.e., visible layer
The learning process of the DBN models has two phases like supervised learning (fine-tuning) and unsupervised learning (pre-training) [
During Gibbs sampling procedure, a likelihood distribution condition of the hidden neuron and visible neurons is represented herewith.
With
In order to achieve the optimum tuning of hyperparameter in DBN method, WOA is implemented. In the initial phase, an initialized procedure takes place. In the surrounding prey model, a humpback whale is noted around the place of prey. The whale starts surrounding the prey. To unclear the place in searching area, the current optimum result is regarded as the prey. If the optimum searching agent is determined, the other searching agent refreshes the condition in the way of optimum searching agent.
Based on the fitness value of above iteration, the coefficient vector “
Spiral procedure is employed amongst the place of whale and prey to mimic the helix-framed development of humpback whale which is given herewith [
It is observed that the humpback whale swims over the prey in constricting circle and also in a wind molded manner.
In order to illustrate this synchronous efficiency, 50% probabilities have been forecasted to have been optimized among the contracting surrounded and spiral schemes of whales. The mathematical expression as:
So, the arbitrary values are employed mainly over 1 or under
The current section discusses the experimental validation of the proposed XAI-ASD technique on the applied benchmark dataset from UCI repository. The details related to the dataset are listed in
No. | Dataset name | Sources | Number of attributes | Number of instances |
---|---|---|---|---|
1 | ASD-children dataset | UCI | 21 | 292 |
2 | ASD-adolescent dataset | UCI | 21 | 104 |
3 | ASD-adult dataset | UCI | 21 | 704 |
Number | Attributes description |
---|---|
1 | Patient age |
2 | Sex |
3 | Ethnicity |
4 | Born with jaundice |
5 | Family member with Pervasive Development Disorders (PDD) |
6 | Who is fulfilment the test |
7 | Country of residence |
8 | Used the screening app before or not |
9 | Screening test type |
10–19 | Based on the screening method answers of 10 questions |
20 | Screening score |
21 | Target class [Yes/No] |
The ASD classification results of the proposed XAI-ASD technique on the applied dataset is given in
Dataset | Sensitivity | Specificity | Accuracy | F-score | Kappa |
---|---|---|---|---|---|
ASD-children | 94.76 | 95.57 | 95.43 | 94.82 | 90.87 |
ASD-adolescent | 92.59 | 98.66 | 96.43 | 92.80 | 90.60 |
ASD-adult | 97.09 | 93.12 | 95.44 | 96.85 | 92.67 |
In order to showcase the effective performance of XAI-ASD technique, a detailed comparative analysis was made against existing ASD diagnosis techniques and the results are shown in
Methods | Sensitivity | Specificity | Accuracy |
---|---|---|---|
XAI-ASD (children) | 94.76 | 95.57 | 95.43 |
XAI-ASD (adolescent) | 92.59 | 98.66 | 96.43 |
XAI-ASD (adult) | 97.09 | 93.12 | 95.44 |
IWOA-FRBC (children) | 93.57 | 93.42 | 93.49 |
IWOA-FRBC (adolescent) | 91.09 | 97.08 | 95.45 |
IWOA-FRBC (adult) | 95.24 | 92.68 | 94.23 |
Decision tree | 53.30 | 54.90 | 54.70 |
Logistic regression | 55.50 | 62.60 | 59.10 |
Neural network | 53.30 | 71.20 | 62.00 |
In current study, a new XAI-ASD technique is designed and validated to detect and classify the different stages of ASD. The proposed XAI-ASD technique encompasses different processes namely pre-processing, WOA-based FS, DBN-based classification, and WOA-based parameter tuning. The use of WOA-FS technique helps in the selection of optimal feature subsets. Besides, the usage of WOA helps in the optimal selection of hyperparameters of DBN model that in turn helps in accomplishing an improved ASD diagnostic performance. In order to validate the ASD diagnostic outcomes by the proposed model, a series of simulations was conducted on benchmark dataset. The experimental results highlight the betterment of XAI-ASD technique over other recent state-of-the-art techniques under different measures. As a part of future work, the proposed XAI-ASD technique can be tested using real-time dataset collected from hospitals and IoT devices.