# Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis
###### Abstract  
In domains that have complex data characteristics and/or noisy data, any single supervised learning algorithm tends to suffer from overfitting. One way to mitigate this problem is to combine unsupervised learning component as a front end of the main supervised learner. In this paper, we propose a hierarchical combination of fuzzy C-means clustering component and fuzzy max-min neural network supervised learner for that purpose. The proposed method is evaluated in a noisy domain (Pima Indian Diabetes open database). The proposed combination showed superior result to standalone fuzzy max-min and backpropagation-based neural network. The proposed method also showed better performance than any single supervised learner tested in the same domain in the literature with high accuracy (80.96%) and was at least competitive in other measures such as sensitivity, specificity, and F1 measure. fuzzy max-min neural network; fuzzy c-means; diabetes; supervised learning; unsupervised learning +
Footnote †: journal: _Applied Sciences_  
For example, in human activity recognition problem, Predict and Cluster method [5] applies clustering algorithm to gather similar activities into the same cluster and different activities into separate clusters. Then, a method based on an encoder/decoder recurrent neural network, can effectively learn distinct actions. It outperforms other single supervised methods in this domain. Recently, for the same domain, a system utilizes K-means first and generate a hyper label that becomes the input set for supervised learner Spatial-Temporal Graph Convolutional Network (ST-GCN) [6]. This approach obtained 9% better result in accuracy than the base supervised algorithm ST-GCN [7] and gives robustness when facing with a new activity by unsupervised component since it can only retain the cluster to which the activity is assigned. Similar efforts to combine unsupervised learning result with supervised learning component shows plausible result in facies prediction problem [8; 9], malware detection [10], land price prediction [11], credit risk assessment [12], miRNA target prediction [13], text summarization tasks [14; 15] and image segmentation task [16]. For those systems, usually there is no restrictions on unsupervised algorithm component, that is, any clustering algorithm can be applied and Support Vector Machine (SVM) [17] is frequently used for supervised learning component [8; 9; 10; 12; 13] but standard neural network [11] and decision tree [16] can be applied as well. Considering that the role of unsupervised learning component is to provide more informative/independent features to the input of the supervised classification component in this hierarchical combination, we can extend the relationship between the input data and output clusters of the unsupervised learning component. If we apply fuzzy clustering algorithms to the unsupervised learning component, that fuzzy clusters maintain the relationship between the cluster and all input with its membership function so that the entire system can maintain more stable and robust relationship between the input data and the final output of the supervised learner. That is, fuzzy clusters maintain multiple associations with input data and the output of unsupervised learning component has overlapped concept from the input data. If such fuzzy clustering component is well combined with the supervised classifier, the entire system might be more flexible, stable, and robust than the standalone supervised learner. There exist only a few studies on that direction. In [18], the SVM classifier generates a spectral-based classification map, whereas Fuzzy C-Means (FCM) is adopted to provide an ensemble of segmentation maps. In traffic flow prediction problem [19; 20], type-2 FCM model classifies the historical data into different traffic flow patterns, and its result becomes the input of the neural network back propagation principle.