## 2 Literature Survey
A new feature selection method that selects 15 most relevant attributes was used to determine an optimal feature subset. The result showed an improvement in the prediction accuracy of dermatological testing data samples. [PERSON] [26], introduced a comparison among different data reduction methods. These data reduction methods were tested against classification algorithms like MLP and KNN algorithms for accuracy. The observation noted that the fuzzy rough feature selection method performed much better than correlation feature selection, gain ratio and principal component analysis methods. [PERSON] [27], developed a hybrid machine learning model to predict diabetes risk disorders. It used K-Means clustering, followed by the C4.5 algorithm for classification. A very high 92.38% accuracy was obtained with this hybrid model. A predictive model for knee joint disorder detection using VAC signals was proposed by [28]. The a priori algorithm and the genetic algorithm were used as feature evaluators while LS-SVM were the classifiers used for the study. A classification accuracy of 94.31% was the outcome when LS-SVM had been integrated with the a priori algorithm and the genetic approach. [PERSON] [29], demonstrated various inter class and probabilistic attribute selection methods based on distance computation. It was used as a pre-processing approach to induce decision trees. The results indicated that the inter class distance parameter generated optimum performance in comparison to probabilistic metrics. [PERSON] et al. [30], developed a hybrid classification model using the genetic algorithm as the wrapper method and the back-propagation algorithm as the classifier in detecting the presence of PIMA Indian diabetes. The hybrid model outperformed the back-propagation algorithm in classification. In [31], a cardiac arrhythmia dataset was used with Information Gain and Relief-F as filter methods. SVM and regression were the classifiers used. The performance was evaluated with accuracy, precision and recall metrics. It was observed that the Relief-F filter method combined with the SVM algorithm produced optimum result. [PERSON] et al. [32], presented a sequential floating forward selection (SFFS) wrapper method to identify an optimal feature set in mental stress dataset. SVM was used for classification and it was observed that the SFFS method combined with the SVM achieved very good accuracy when compared to other algorithms such as the genetic algorithm and information gain. The combination of Principal component analysis (PCA) with the Relief-F method was applied to a breast cancer and diabetes dataset in [33] and evaluated with the KNN algorithm. The classification performance was compared with other filter methods like correlation-based feature selection and information gain. The hybrid combination of PCA and Relief-F methods achieved a higher accuracy than others. [PERSON] et al. [34], used a coronary artery disease dataset and applied an embedded model of gain ratio and chi-square filter approaches to rank attributes and remove the irrelevant features. Then, the attributes are classified with Random Forest, SVM and bagging algorithms. It was noted that the hybrid model produced an impressive result while classification with random forest achieved the highest accuracy with lowest execution time. The authors of [35], presented four different filter approaches such as gain ratio, Relief-F, information gain and chi-square methods on a tumour dataset, to select the most relevant attributes for the SVM algorithm. Classification with SVM and gain ratio gave the best performance in terms of accuracy rate and precision value. [PERSON] et al. [36], proposed a new embedded model that integrates information gain and a wrapper approach which used a decision tree classifier on various disease datasets like diabetes, lung disease, hepatitis and heart disease. A classification accuracy of 92.86% was achieved with the reduced attribute set.