Classification and action rules in identification and self-care assessment problems

1.Introduction

A disability is a limitation or lack of ability to perform an activity in the manner or to the extent considered normal for a person, resulting from damage to and impairment of bodily functions. According to the World Health Organization (WHO), persons with disabilities are defined as those who are unable to provide for themselves, partially or wholly, the ability for normal personal and social life as a result of congenital or acquired physical or mental impairment [1]. The division of the effects of disease into damage, incapacity, and role limitation has been accepted. Damage refers to abnormalities in the structure and appearance of the body and the function of organs or systems, regardless of the cause. Thus, it means a disorder at the organ level. Incapacity reflects the effects of damage on a person’s performance of activities and actions. Role limitations are the consequences of damage and incapacity that affect interpersonal interactions and adaptation to the environment. Persons under the age of 16 are counted as disabled if they have a physical or mental impairment with an expected duration of more than 12 months. This impairment may be due to a congenital defect, long-term illness or bodily injury. This makes it necessary to provide these children with total care or assistance in meeting the basic needs of life in a manner that exceeds the support needed by a person of their age. Persons under the age of 16 are counted as disabled if they have a physical or mental impairment with an expected duration of more than 12 months. This impairment may be due to a congenital defect, long-term illness or injury and in many cases has a major impact on the child’s progress through the developmental stages. These limitations make it necessary to provide these children with total care or assistance in meeting the basic needs of life in a manner that exceeds the support needed by a person of their age. Therapy, a multi-pronged process that focuses on physical activity, intellectual development and proper social functioning, is therefore very important. Along with the support of loved ones, this is often the basis of life for children with disabilities. Children with disabilities very often experience problems in their lives with performing basic everyday activities. Therefore, self-care is a very important issue, which more or less ensures independence and comfort in everyday life. It is crucial to choose the right therapy, which can only be achieved with a proper diagnosis and classification of the disability [2, 3, 4, 5, 6].

Diagnosing disabilities in children is, in many cases, a very elaborate process that requires a great amount of knowledge, experience, and collaboration among physicians, physical therapists, neuropsychologists, therapists, special educators, and other professionals. A very important aspect here is the identification of individual characteristics of disabilities, as well as their appropriate classification. The International Classification of Functioning, Disability and Health of Children and Young People version (ICF-CY) may be helpful for this goal [5]. ICF-CY is based on the International Classification of Functioning, Disability and Health (ICF) and thus uses common language and terminology to identify problems with disability. However, the ICF-CY delineates the child in relationship to the environment, and specifically the child in the context of the family. It is a modern, family-centred approach that recognises that it is impossible to observe, diagnose, treat, educate and support a child in isolation from the family situation. This affords an opportunity to plan and take effective action for the child in conjunction with an action to strengthen the family. The ICF-CY can be used to describe functioning and disability in children and to classify health and related conditions. The ICF-CY does not classify children. It enables a classification of the characteristics of children’s health status in the context of individual life situations and environmental influences. It provides an opportunity to plan and implement effective support for children with disabilities. The ICF-CY can be used by all health care providers and consumers, and by all those who are involved in health, education and care of children and young people and seek to create optimal conditions for their development [2, 3, 4, 5, 6].

The ICF-CY classification is divided into two parts (functioning and disability and contextual factors), each of which is divided into two classification components (body functions and structures, activity and participation, environmental factors and personal factors, respectively). Problems related to self-care will be found in the section of activity and participation. They mainly concern activities such as daily toileting, caring for body parts, grooming, etc., which are the main activities of daily life that should be fully developed by the age of about 7 years. The development of these daily living skills is influenced by contextual factors, that is, environmental factors and personal factors [2, 3, 4, 5, 6].

The analysis of self-care meaning shows that this ability has an important role in the quality of life of the children with physical and motor disabilities. The obtained information becomes valuable. Thus, it is very important to study and classify children’s self-care problems. Action rules can reclassify patients from one group to another; therefore can be helpful in treatment methodology.

Literature survey uncovers a number of different, but also related, methods for knowledge extraction from datasets of children with disabilities. The authors mainly study the efficiency of selected classification algorithms, and investigate the effect of PCA (principal component analysis) on classification accuracy, as well as find some decision rules [3, 4, 6, 7, 8, 9]. For example, Sharma [3] used Partitioned Multifilter with Partial Swarm Optimization to feature selection and then applied Naïve Bayes, Multilayer Perception, C-4.5 and Random Tree classifier receiving 64–80% accuracy. Islam et al. [9] applied Principal Component Analysis and used Extreme Learning Machine, K Nearest Neighbour, Support Vector Machine, Artificial Neural Network, Random Forest, as well as Gradient Boosting. More advanced analysis can be found in the work of Fatemi Bushehri and Zarchi [4]. They proposed an expert model based on Probabilistic Neural Network and Genetic Algorithm, and also used CART algorithm to find classification rules. In our work, we go a few steps further. In addition to feature selection and classifier evaluation of the obtained models, we will extract not only classification rules, but also action rules which have not been implied before in any works related to self-care problem.

The purpose of this paper is to extract classification rules and extract action rules that can be helpful for diagnosticians and physiotherapists who work with children with disabilities. This study can put a focus on those areas of self-care problems where even a minimal reduction of features would be of great benefit to the child.

2.Materials and methods

For research we used dataset SCADI of 70 children with physical and motor disability [4, 10]. These data were obtained from UCI Machine Learning Depository [11]. The SCADI database was compiled and published in collaboration with occupational therapists as the first standard set of data on self-care activities of children with physical and motor disabilities [10]. The dataset contains information on 70 children aged 6 to 22 years (29 female and 41 male) from 3 health and educational centres in Iran. The data were collected from 2016 to 2017. The following diseases were diagnosed in the studied children: [10]:

Each child was characterized using 206 features: gender (1 – male, 0 – female), age, self-care activities based on International Classification of Functioning, Disability and Health of Children and Young People (202 features shown in Table 1: 1 – the case has this attribute, 0 – otherwise) and an attribute that classifies the child into a specific group [10]:

The first step of the study was feature selecting and checking its impact on the accuracy of classifying children to particular groups. The following methods were used for attribute reduction:

The second step of the research was building models that included all attributes and attributes extracted using feature selection. Then we applied the classification to check if feature selection has brought the expected results. The methods chosen for classification are those which allow to build the decision rules in a simple way, i.e. [20, 21, 22, 23, 24]:

We used a 10-fold cross-validation for testing, training and validation. Cross-validation is a statistical method that divides a statistical sample into k subsets, then performs an analysis on some of them and uses the others to confirm the reliability of the results. The k times this process is repeated, each time the test set is changed. In this way, within one repetition, k-1 sets are analysed. The analysis is performed k times, and averaging k results allows one final result [25].

To evaluate the above classifiers, we have chosen ACC (Total Accuracy) measure, which is the accuracy that specifies the probability of correct classification, that is, the ratio of correct classifications to all classifications. [24, 26, 27]:

where: T⁢P (True Positive) is a correctly classified tuple belonging to the class of interest, T⁢N (True Negative) is a correctly classified tuple belonging to another class, F⁢P (False Positive) is a misclassified tuple actually not belonging to the class of interest and F⁢N (False Negative) is a misclassified tuple that actually belongs to the class of interest.

We also collated such measures as kappa statistic, mean absolute error, root mean squared error, relative absolute error, as well as root relative squared error [24, 26, 27]:

For all analyses and calculations was used WEKA software (version 3.8.4., Machine Learning Group, University of Waikato, New Zealand). WEKA is a machine learning and data mining software developed by researchers at the University of Waikato in Hamilton, New Zealand [27, 28].

The final steps of this study were construction of classification rules and extraction of action rules from classification rules. Classification rules are of interest to most data explorers to summarize the discriminant power of classes found in data. A classification rule is a statement that distinguishes the concepts of one class from other classes. They describe the relationship between conditional attributes (usually many) and the decision attribute by means of implications: on the left side there are conditions expressed by a certain logical formula, on the right – the value of the decision attribute. Rule-based classification systems typically contain many rules, each of which may be based on a different set of conditional items [29, 30, 31, 32, 33, 34, 35].

More advanced are action rules, which provide guidance on possible actions the user should take to achieve the desired result. Action rules were first proposed by Raś and Wieczorkowska [36] and they are constructed from classification rules which suggest ways to re-classify objects, for example reclassify patients to a desired group. An action rule can be presented in the following form [32, 34, 37]:

where ω indicates a fixed condition features conjunction, that is a part of both groups, (α→β) is recommended changes in flexible features value and (Ψ→Ω) means an effect of the action, which the user wants to achieve. All action rules can be evaluated because of its support and confidence [24, 26].

3.Results and discussion

The results of feature selection are shown in Table 2. The top 20 attributes were selected for each feature selection method. Then the obtained attributes were compared and only those that were repeated in each method or in at least two methods were selected for further study. The remaining attributes were removed. It is worth noting that as many as 17 attributes out of 20 were extracted in each of the selected feature selection methods. The selected attributes were: d5400-4, d5402-4, d5102-4, d5101-4, d5401-4, d5403-4, d5203-4, d5204-4, d5201-4, d5202-4, d5501-4, d53001-4, d53011-4, d5205-4, d5602-4, d5100-4, d5403-2, d53011-2 and d53001-2.

Table 2

Results of feature selection

The same attributes obtained in each method are marked with the same symbols. Attributes that repeat in only two methods are highlighted. Non-recurring attributes were crossed out. Attributes: d5400-4 – putting on clothes with complete impairment, d5402-4 – putting on footwear with complete impairment, d5102-4 – drying oneself with complete impairment, d5101-4 – washing whole body with complete impairment, d5401-4 – taking off clothes with complete impairment, d5403-4 – taking off footwear with complete impairment, d5203-4 – caring for fingernails with complete impairment, d5204-4 – caring for toenails with complete impairment, d5201-4 – caring for teeth with complete impairment, d5202-4 – caring for hair with complete impairment, d5501-4 – carrying out eating appropriately with complete impairment, d53001-4 – carrying out urination appropriately with complete impairment, d53011-4 – carrying out defecation appropriately with complete impairment, d5205-4 – caring for nose with complete impairment, d5602-4 – indicating need for drinking with complete impairment, d5100-4 – washing body parts with complete impairment, d5403-2 – taking off footwear with moderate impairment, d53011-2 – carrying out defecation appropriately with moderate impairment, d53001-2 – carrying out urination appropriately with moderate impairment, d5200-4 – caring for skin with complete impairment, d5602-2 – indicating need for drinking with moderate impairment, d53010-4 – indicating need for defecation with complete impairment, d53000-4 – indicating need for urination with complete impairment, d5400-3 – putting on clothes with severe impairment.

Table 3 contains classification results for the model where all available attributes are considered (205 + 1 classifier). We can observe, that the best classifier for this model is the PART algorithm. Almost 81.5% of instances were correctly classified here. Equally strong classifiers (80% of correctly classified instances) are J48 and CART algorithms, as well as Random Tree (over 77% of correctly classified instances). The weakest classifier was the JRip algorithm, which achieved an ACC score of about 71.5%.

Table 4 contains the quality parameters for the model containing the attributes extracted during feature selection (19 attributes + 1 classifier). Each of the selected classifiers achieved over 70% accuracy. The best algorithm, with over 77% correctly classified instances, was CART. Almost 67% of correctly classified instances were obtained using the JRIP algorithm, while the PART and J48 algorithms allowed to correctly classify more than 74% of instances. The weakest classifier was Random Tree with 70% ACC.

When comparing the results of the two models (Table 5) we can observe, that for most of the selected classification algorithms, the results for the model after feature reduction are slightly worse than for the model containing all available attributes. Better results are obtained only for the JRip algorithm. However, we cannot say here that feature selection does not make sense. The percentage differences in ACC here range from about 3 to about 7 percentage points. Considering the fact that we reduced 205 attributes to 19, we can assume this was a promising result. Attribute reduction may be helpful for diagnosticians and physiotherapists for faster diagnosis and classification of self-care problems in children with disabilities.

As mentioned earlier, the selected classifier algorithms allowed us to extract classification rules. Classification rules were compared for the model containing all features and the model after feature extraction. In most cases, the rules overlapped, demonstrating the benefits of feature selection. Since about 100 classification rules have been extracted, in this paper we will only present some of the rules for the model after feature reduction. These rules are written below, while the underlined rules were also present in the model before feature selection:

We can see that no classification rule was found for Group 3 for the model after feature selection. For the model with all features, only one rule was found for Group 3: IF d5501-1 = false AND d5202-4 = false AND d53010-0 = true AND d5205-2 = true AND Gender = male THEN class = Group 3.

The rules for classification into Group 7 are very important for diagnosis and physical therapy. As we can see, for the model after feature selection, these rules apply only to the negation of features associated with total impairment. Usually such impairment cannot be removed completely, it can only be reduced to some extent. Therefore, it is useful here to show the rules obtained for all available features:

The final stage of the study was to extract action rules that would allow specific patients to be reclassified from one group to another. Below there are some selected action rules extracted from the classification rules – both for all attributes and for the model after feature selection:

These rules are written in the form of a specific scheme. For example, the last one can be interpreted as follows: If carrying out eating appropriately is mild impairment, caring for hair is severe impairment and we change putting on clothes to moderate impairment, then we can reclassify patient from Group 5 with washing oneself, caring for body parts, toileting, and dressing problem to Group 1 with only caring for body parts problem.

4.Conclusion

Children with disabilities face many problems in their daily lives. Undoubtedly, a very important aspect here is the problem of self-care and its diagnosis. It is therefore necessary to support diagnosticians and physiotherapists in recognizing self-care problems in children. Data mining, which is being used more and more in medicine and related fields, can be helpful here. Appropriate data analysis allows building models and algorithms that can support the recognition of self-care problems in children with disabilities. Decision and action rules presented in this paper may be equally useful. They can direct therapists to those areas of disability where even a minimal reduction would be of great benefit to the disabled child.