# Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis
## 4 Discussion  
In supervised learning, model overfitting is a hurdle to make the learner to be robust and stable over unseen data. Over-fitted supervised learners tend to memorize more than necessary including noise on the training set, instead of learning the discipline hidden behind the data [3]. Statistically, overfitting occurs when we use a model that is more flexible than it needs to be, or the model includes irrelevant components [29]. Noise, missing values, insufficient information representation of the input, and unnecessary input parameter correlation can be the cause of such undesirable phenomenon in supervised learning. Combining unsupervised learning components as a preprocessing unit of the supervised learning component may mitigate such model overfitting problem since the result of unsupervised learning (clusters) can represent an intermediate concept or noise filtering scheme for the supervised learner. Such attempts have shown encouraging result in many complex machine learning problems that have high uncertainty and noisy data in engineering and management [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. In this paper, we propose a hierarchical combination of fuzzy unsupervised learning component (FCM) and supervised learning component (FMM: Fuzzy Max-min Neural Network) in diabetes diagnosis that is known as a highly noisy domain since it has not much labelled data and has many missing values in the dataset. We expect that clusters produced by the FCM would give more mutually independent input to the supervised learning component (FMM) so that the performance of the proposed approach can be more robust than the standalone supervised learner. In experiment using the 30-fold 7:3 random stratified holdout procedure, we report that the proposed FCM-FMM combination is better than standard back propagation-based neural network and standalone FMM as expected in all measures except the _sensitivity_. FMM that shows high sensitivity is extremely poor in _specificity_ in that it generates too many false positives. The performance of our proposed FCM-FMM combination is compared with various supervised algorithms using the same PID database and the same measures (_sensitivity_, _specificity_, _accuracy_, _F1 score_) reported in other studies. FCM-FMM is the best in sensitivity and the accuracy and the second in F1 score and Naive Bayes is the best in other two mea \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & _Sensitivity_ & _Specificity_ & _Accuracy_ & _F1 Score_ \\ \hline Naive Bayes [26] & 75.9 & 76.3 & 76.3 & 76.0 \\ SVM [26] & 42.4 & 65.1 & 65.1 & 51.3 \\ Decision Tree [26] & 73.5 & 73.8 & 73.8 & 73.6 \\ Soft Voting [27] & 70.0 & 73.1 & 79.1 & 71.6 \\ FCM-FMM & 78.8 & 70.3 & 81.0 & 74.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison summary from the literature. sures among five algorithms thus we can conclude that this fuzzy hierarchical combination is at least competitive in the highly noisy problem domain such as Diabetes diagnosis. However, the limitation of this work should be noted that the performance of the proposed algorithm is tested on the single open dataset (PID database). Although the PID dataset chosen in this paper is a good example of testing the robustness of the learning algorithms under fuzzy insufficient input information with many missing values, any direct generalization of this experiment result may not be appropriate to judge the overall performance of tested algorithms. The proposed method should be tested for many other noisy datasets to prove its efficacy in the future. Additionally, in model validation, we used repeated hold-out for simplicity with the expense of pessimistic bias of the performance partly because two studies we compared in Table 8 took different policies (10-fold cross validation in [26] and repeated holdout in [27]).