1. Introduction
Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners with decision-making about diagnosing some diseases by managing data or medical knowledge [
1]. Within these CDS systems, we can find several techniques: Machine Learning (ML) [
2], Deep Learning (DL) [
3], and Fuzzy logic (FL) systems [
4,
5]. According to Reference [
2], among the first techniques, we can find k-Nearest Neighbor—k-NN, Artificial Neural Network—ANN, Decision Tree—DT, Support Vector Machine—SVM, random forest, among others. Within the second one, we can find Convolutional Neural Networks (CNN) [
6,
7,
8], Constructive Deep Neural Network (CDNN) [
9], Deep Neural Network (DNN) [
10], a Deep Belief Network (DBN) [
11], a Deep Boltzmann Machine (DBM) [
12] as well as others. For the third category, according to Reference [
4], we can find Fuzzy Expert Systems (FES), the Fuzzy Set Theory (FST), the Fuzzy Inference Systems (FIS), the Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Fuzzy Neural Networks (FNN), fuzzy cognitive maps, and more.
Decision Support Systems (DSSs) are, therefore, solutions that serve the management in their decision-making process. DSS, by default, comprise interactive features to aid enough data and model analysis with the intent to classify and resolve predicaments as well as present resolutions [
13]. According to Turban et al. [
14], all DSS comprises four standard components: information, model, knowledge, and user interface management sections. The first component Information or Data administration focuses on the storage, manipulation, and maintenance of necessary information. The sources include either one or a combination of the following: The primary source of information, external sources, and personal information. The second component, which is the model, refers to the analytic tool that is the part responsible for the analysis. It includes financial, statistical, management science, or other quantitative models that support data analysis as well as system maintenance. The third component is the knowledge-based (KB) management subsystem. It provides information about data and their relationships, particularly complex data. It comprises possibilities, alternatives, rules, and methods for evaluating options to arrive at a decision. The fourth element is the user interface subsystem. The sole intent of the user interface is to present a mechanism within which the user and the DSS may communicate. DSS is interactive and, therefore, requires massive user communication [
14].
For Clinical Decision Support Systems, many predictive or classification algorithms have been implemented to diagnose different diseases [
15]. Among them, we can find works using fuzzy rule miner [
16], Constructive Deep Neural Network [
9], Support Vector Machines [
17,
18], ANFIS [
19,
20], genetic algorithms [
21], random forest [
22,
23], Decision Trees [
24,
25], k-NN [
24], and more. Most of these intelligent systems have their algorithms for “learning” from the data. For example, artificial neural networks “learn” using layers (inputs, hidden and output layers) and the Back Propagation—BP algorithm. Their learning process is made through the adjustment of weights and bias on every single layer. Hybrid techniques use the learning strength of the mentioned technique and adjust the parameters of other intelligent systems. For example, a neuro-fuzzy system uses the potential of artificial neural networks to adjust the parameters of a Sugeno-type fuzzy inference system.
For the development of intelligent systems using fuzzy logic, two primary components are needed including the knowledge database and the knowledge rule base. Mamdani-type fuzzy logic does not have an algorithm to “learn” their knowledge components (Database and rule base) from the data. It means that this type of fuzzy logic must be carried out manually with a high subjective element, that is, it is the modeler or specialist who determines the number of the fuzzy sets for each input and output variable establishing the ranges between these sets. The modeler or specialist also defines the set of rules that the fuzzy inference system will have. For this reason, the whole proposition of the system becomes subjective.
Another critical component that we need to point out is the rules number in the decision support system. This component is influenced by the number of variables that are required to provide the best performance. This component is known as a feature selection. For this, the modelers can use pre-processing techniques to help establish these characteristics. However, it would be time to add one more component to the proposed framework, which can increase the system’s processing time.
One problem about the learning process among some computational intelligence techniques is that they are considered “black-boxes” and there is no transparency in the training process. A decision support system based on an artificial intelligence technique must have the ability to explain the decision made and the process used for it. For the reasons above, it is essential to look for a fast and effective alternative that could “learn” from the data, and automatically generate the Mamdani-type fuzzy inference system transparently, mapping the interactions between variables (inputs) and the relationships with the output variables. Thus, the primary aim of this work is designing, implementing, and validating a framework for the development of data-driven Mamdani-type fuzzy Decision support systems using clustering and pivot tables to gather the mentioned components that are being obtained manually and subjectively.
As mentioned before, clinical decision support systems help clinicians and practitioners for decision-making about diagnosing some diseases. For this work, we selected three medical fields for the implementation and validation of the proposed framework: Breast cancer, Wart tumors, and Caesarean section.
Breast cancer is the most common and deadly cancer for women [
26]. The symptoms may vary from person to person. However, this cancer has the following symptoms: lump in the breast, changes in breast shape, skin dimpling, orange like a scaly patch of skin, and more [
27]. Approximately 40,920 deaths from invasive female breast cancer are predicted in the United States by the American Cancer Society (ACS) [
28]. According to Reference [
29], the Wisconsin Breast Cancer dataset was collected from the patients of University of Wisconsin-Madison Hospitals. The number of observations of the dataset is 699 data pair. The dataset has missing values. It is comprised of 458 benign cases and 241 malign cases. The descriptive statistics of the dataset can be found in Onan [
26]. The input variables are 9, and the output variable is 1 (Breast cancer classification—benign and malignant).
The attributes of this dataset are [
29]:
Attribute number | Domain |
1. Clump Thickness—CT | 1–10 |
2. Uniformity of Cell Size—UCSi | 1–10 |
3. Uniformity of Cell Shape—UCSh | 1–10 |
4. Marginal Adhesion—MA | 1–10 |
5. Single Epithelial Cell Size—SECS | 1–10 |
6. Bare Nuclei—BN | 1–10 |
7. Bland Chromatin—BC | 1–10 |
8. Normal Nucleoli—NN | 1–10 |
9. Mitoses—Mi | 1–10 |
10. Class: | (2 for benign 4 for malignant) |
The literature shows that there are a lot of research studies about the breast cancer diagnosis using this Dataset (WBCD). Among the most recent works, we can find the followings: Liu, Kang, Zhang, and Hou [
6] who reported that they were seeking to identify ways to improve the classification performance for this dataset based on convolutional neural networks (CNN). Their results were 98.71% for classification accuracy, a sensitivity of 97.60%, and a specificity of 99.43% for WBCD. The partition data method used by the authors was the five-fold cross-validation. Karthik et al. [
10] used deep neural networks for the classification task and reported a classification accuracy of 98.62%. The dataset was partitioned using random sampling. Abdel-Zaher and Eldeib [
11] used a deep belief network unsupervised path followed by a back propagation supervised path. Their results were 99.68% for classification accuracy, 100% for Sensitivity, and 99.47% for specificity. The used data partition method was random sampling. Onan [
26] presented a classification model based on the fuzzy-rough nearest neighbor algorithm, consistency-based feature selection, and fuzzy-rough instance selection. This model obtained a classification accuracy of 99.71%, a sensitivity of 100%, and a specificity of 99.47%.
Regarding the Coimbra breast cancer, the dataset was collected from women newly diagnosed with breast cancer from the Gynecology department of the University Hospital Center of Coimbra between 2009 and 2013 [
17]. This dataset comprises 116 instances (64 patients and 52 healthy volunteers) and nine input variables. The goal is to predict the presence of breast cancer in women. The descriptive statistics of the clinical features are presented in
Table 1.
For the Coimbra Breast cancer dataset, Patrício, Pereira, Crisóstomo, Matafome, Gomes, Seiça, and Caramelo [
17] used the Support Vector Machine, logistic regression, and random forest as classifiers. The authors used four input variables (glucose, resistin, age, and BMI) as predictors for the presence of breast cancer in women. The best results were obtained for SVM. Sensitivity results ranging between 82% and 88% and specificity ranging between 85% and 90%. The 95% confidence interval for the AUC was [0.87,0.91]. This study used 20-fold Monte Carlo Cross-Validation through 100 iterations. Polat and Sentürk [
30] proposed a new machine learning-based hybrid method. They combined the normalization, feature weighting, and classification for the same dataset. The hybrid approach achieved 91.37% for accuracy.
Warts are benign tumors caused by infection with human papillomavirus (HPV) and can be categorized into three types: Cutaneous, Epidermodysplasia Verruciformis (EV), and Mucosal [
25]. According to Al Aboud and Nigam [
31], this medical problem affects approximately 10% of the population. The authors state that, in school-aged children, the prevalence is as high as 10% to 20%. The literature shows that there are two treatments: cryotherapy and immunotherapy. Many studies have been done to predict the appropriate treatment for each symptom. Among these works, we can find the followings: Khozeimeh, Alizadehsani, Roshanzamir, Khosravi, Layegh, and Nahavandi [
19] identified appropriate treatment for two common types of warts (plantar and common), and they tried to predict the responses of two of the best methods for the treatment using a Fuzzy logic rule-based system. This study was conducted on 180 patients, with plantar and common warts, who had referred to the dermatology clinic of Ghaem Hospital, Mashhad, Iran. In this study, 90 patients were treated by the cryotherapy method with liquid nitrogen and 90 patients with the immunotherapy method. The selection of the treatment method was made randomly. The original first method dataset consists of seven features (six inputs and one output). The second one has eight features (seven inputs and one output variable). The classification attribute (label) is a response to treatment. The observed features in the clinical experiments are presented in
Table 2.
The results show that the classification accuracy for immunotherapy and cryotherapy was 83.33% and 80.7%, respectively. As a data partition method, the authors used 10-fold cross-validation. Nugroho, Adji, and Setiawan [
23] combined the two datasets to produce a single prediction method. As classifiers’ methods, they used the C4.5 algorithm combined with Random Forest Feature Weighting (RFFW) (C4.5+RFFW) to select the relevant features to improve accuracy. According to the results, the single prediction increases the classification accuracy to 87.22%. The authors used 10-fold cross-validation. Basarslan and Kayaalp [
24] applied Naïve Bayes, C4.5 decision tree, logistic regression, and the k-nearest neighbor as classifiers for the dataset. The authors used 10-fold cross-validation. The results show that the C4.5 decision tree obtained the best performance for both datasets. The classification accuracy for cryotherapy dataset was 93.3% (raw data) and 95.4% (after correlation). For the immunotherapy dataset, the classification accuracy was 82.5% for raw data, and 84% after correlation. Jain, Sawhney, and Mathur [
22] applied random forest (RF), binary gravitational search algorithm (BGSA), and the hybrid between these two techniques. Their results show that the highest classification accuracy for both datasets was 88.14%, and 94.81% for immunotherapy and cryotherapy, respectively, using BGSA + RF.
According to the World Health Organization (WHO) [
32], the cesarean sections are associated with short-term and long-term risk, which can extend many years beyond the current delivery, and affect the woman’s health, her child, and future pregnancies. These risks are higher in women with limited access to comprehensive obstetric care. According to Reference [
33], the mean caesarian delivery rate in the United States of America for 2017, was 32% for all births, showing a high percentage of use for this medical procedure, due to the international healthcare community that has considered the ideal rate for cesarean sections to be between 10% and 15% [
32]. The Caesarean section dataset is comprised of five input variables and one output variable, and 80 instances. The dataset was collected using pregnant women’s information and the referred delivery in the Tabriz health center. Gharehchopogh et al. [
34] worked with a C4.5 Decision Tree algorithm. The authors did not mention the data partition method. Their results for classification accuracy was 86.25%. Amin and Ali [
35] utilized k-nearest neighbor, Support Vector Machine, Naïve Bayes, logistic regression, and random forest as classifiers. The authors did not mention the used data partition method. Their result is classification accuracy of 95% obtained for random forests and k-NN.
In all cases, it is critical to developing some tools (models) that assist in decision-making for early detection, appropriate therapy, and treatment [
36]. For the reasons above, the main aim of this research is to develop classification models with high accuracy for the mentioned medical issues.
The originality of the current framework could be considered as follows:
None of the feature extraction methods were applied before the cluster’s analysis.
None of the machine learning algorithms was used or applied.
Clustering was the unique data mining technique that we used.
The principal contributions of this work could be considered as follows:
We present a novel method for knowledge discovery through an easy extraction of the knowledge database and knowledge rule base for the development of Mamdani-type Fuzzy Inference systems.
Using clustering and pivot tables in classification problems allows knowledge discovery through patterns’ recognition. These two tools can increase the likelihood of reducing input variables (feature extraction), which simplifies the decision-making process.
The use of the two tools could be considered a deep machine learning algorithm.
The remainder of this paper is organized as follows.
Section 2 discusses the theoretical background.
Section 3 shows the material and methods (including the proposed framework).
Section 4 presents the research findings.
Section 5 describes the discussion and
Section 6 provides the final conclusions of this paper.
6. Discussion
In this research study, a framework for the development of data-driven Mamdani-type fuzzy clinical decision support systems was proposed. With the objective for validating the results, different case studies were chosen and compared with other studies found in the literature. The case studies were carefully chosen because they are recent, and the authors compared their results with other previous research papers with other computational intelligence, statistical or data mining techniques, obtaining good performance in the classification tasks using the same selected datasets.
Regarding the Wisconsin Breast Cancer Dataset (WBCD). For the WBCD, the best performance from the mentioned authors in
Table 9, belong to Onan [
26], and Abdel-Zaher and Eldeib [
11]. The first author used a classification model based on the fuzzy-rough nearest neighbor algorithm, consistency-based feature selection, and fuzzy-rough instance selection for a medical diagnosis. The second author proposed integration between Wavelet Transformation (WT) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) with the aim to cope with both high-dimensional data challenge and uncertainty. Despite these mentioned results, it can be observed that none of the research works mentioned above reach the accuracy obtained in the current study with the specified number of variables (five). The proposed models with five variables exhibited the best performance indices related to the Wisconsin Breast Cancer dataset, which surpasses the results of advanced techniques (deep learning) such as Deep Belief Network, Deep Neural Network, and Convolutional Neural Networks. The achieved results for the metrics: sensitivity, specificity, F-measure, and Kappa statistics are closer to 1, which indicates a robust fit between the predicted classified data and the observed data. The area under the curve for this dataset is between 0.90 and 1.0, which means an excellent classification task [
93]. The selected variables for both data partition methods were: Uniformity of Cell Size (UCSi), Marginal Adhesion (MA), Single Epithelial Cell Size (SECS), Bare Nuclei (BN), Normal Nucleoli (NN), which indicates that it is not necessary to make the mitosis process and reduces the processing time for the diagnosis and accelerates a possible treatment.
Similar to previous cases, for the Coimbra Breast Cancer dataset, the performance for this case study was higher than the obtained works from the literature. All metric values for both data partition methods are closer to 1, and the Kappa statistic is higher than 0.80, which represents a strong agreement between the classification predictions and the observed dataset. The selected variables for random sampling were all variables such as for cross validation, which were Body Mass Index—BMI (kg/m2), Glucose (mg/dL), Insulin (µU/mL), Homeostasis Model Assessment—HOMA, Leptin (ng/mL), Adiponectin (µg/mL), Resistin (ng/mL), and Monocyte Chemoattractant Protein 1 MCP.1. The authors concluded that, using four variables (Age, BMI, glucose, and Resistin), they could predict the presence of breast cancer in women with sensitivity ranging between 82% and 88% and specificity ranging between 85% and 90%. Our results are higher than those proposed by the mentioned authors.
Regarding the Wart treatment (cryotherapy dataset) presented in
Table 11, it can be observed that the evaluation metric values obtained by the proposed framework (DDFCDSS) performs excellently with the highest classification accuracy (100%) for both data partition methods. This value is the most top predictive classification performance among the reported classifiers obtained from the literature. The values for the other metrics are 1, which represents an excellent fit between the classified data and the observed data. The selected variables for these results are: age, time elapsed before treatment (months), number of warts, type of warts (count), and surface area (mm
2).
Like the previous case, for the wart treatment immunotherapy dataset (
Table 12), for both data partition methods, the evaluation metrics values are the highest for the predictive performance among the other classifiers mentioned in the literature. In this case, the random sampling method worked with only the three variables (time elapsed before treatment, duration diameter of the initial test, and type of warts) mentioned by Reference [
19]. For the cross-validation method, the future extraction proposed in this framework was used. Five variables were selected. The best performance for this training process was obtained for the following variables: age, time elapsed before treatment (months), type of warts (count), surface area (mm
2), and induration diameter of the initial test (mm).
For the cesarean section dataset, the classification accuracy of our framework for the random sampling method is equal to the results shown by Reference [
35]. The authors used different supervised machine learning classifiers such as support vector machines, naïve Bayes, logistic regression, k-nearest neighbor, and random forest. The classification accuracy for the best two models (the last two techniques) was 95%. For the cross-validation method, our results were higher than the results shown by Reference [
34]. However, these results were lower than the results shown by Reference [
35]. Our approach got an area under the curve of 0.9565 for random sampling and 0.93 for cross-validation methods, which indicates an excellent classification task. The Kappa statistics value is also very close to 0.90 for both data partition methods, and they were higher than 0.80, which represents a good fit between the observed data and the predicted ones. These results show that our algorithms can compete with other supervised machine learning classifiers.
The results shown in all mentioned tables (
Table 9,
Table 10,
Table 11,
Table 12 and
Table 13) indicate that the proposed framework for the development of data-driven Mamdani-type fuzzy clinical decision support systems can be used as a reliable approach for helping in the decision-making processes.
A possible explanation for all these results is that pivot tables can characterize every single rule among the proposed layers for each selected problem. In fuzzy inference systems, this is a fascinating result because one rule can significantly affect (positively or negatively) the other rules’ performance. Furthermore, pivot tables can be able to find the non-linear function into the training data set without using any mathematical concept like descendent gradient, least square, or other optimization function. This result is significant because most of the used classifiers utilize some of these optimization functions.
According to these results, it could be demonstrated that the knowledge database and the rule base can be extracted transparently. In this way, the final decision support system shows the interaction or relationships between inputs and output variables. The novelty of this work can be considered as follows. We do not need to calculate any weights, bias adjustment, or optimization function to determine these relationships. The pivot tables are effectively able to capture relevant high-level abstraction and characterize a training data set, which offers insights about how to arrive at a conclusion or decision.
7. Conclusions
This paper presented a detailed framework for the development of data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivots tables. The main contribution of this study is two-fold. First, the methodology was successfully employed to extract significant features from low-dimensional and high-dimensional datasets. Second, clusters and pivot tables can help obtain the knowledge base, and the pivot table can help extract the fuzzy rule base from the inputs/outputs data in a natural way. These two components represent the knowledge-based management subsystem in a Decision Support System, which provides information about data and their relationships helps to arrive at decisions. In addition to the above, we can conclude that:
1. The developed data-driven Mamdani-type fuzzy inference models (experimental results) indicate that it can be used as a classifier because they obtained very promising evaluation metrics. The Performance metrics, specificities, sensitivities, F-Measures, areas under the curve, and Kappa statistics for all datasets: Wisconsin and Coimbra Breast Cancer Datasets, wart treatment immunotherapy and cryotherapy) and the Cesarean section dataset were higher, which is very close to 1 and allows an excellent performance.
2. The experimental results indicate that all the developed data-driven Mamdani-type fuzzy clinical decision support systems can be used as tools for automated diagnosis or decision-making processes.
3. The current framework provides a real pattern for the development of data-driven Mamdani-type fuzzy clinical decision support systems for classification problems.
4. We could state that this framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction [
96] and use a cascade of multiple layers of non-linear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.
Several cons may exist in this research. First, this framework only worked with two kinds of membership functions: trapezoidal and triangular. An idea as future work is working with other membership functions (i.e., Gaussian, gbell, smf, etc.). Second, this framework only worked with two kinds of clusters: The Ward method and k-means. The idea for other future works is to use different types of clusters approaches or methods like the
k-Nearest Neighbors (k-NN) or enhanced k-NN proposed by Reference [
97], Fuzzy C-means, and others. Third, the number of rules can vary depending on the problem. It depends on the cluster number sizes and the number of repetitions of the variable’s values (number of rows for the pivot tables). An idea for future work is to use pivot tables for optimizing the number of rules for every data-driven Mamdani-type fuzzy Decision Support system developed with this framework.
The main future work is to apply this framework in two fields: big datasets (big data) and regression problems. In addition, we would like to use it for the development of data-driven Mamdani fuzzy clinical decision support systems for imaging problems.