2. Related Work
For automatic detection of normal and Coronary Artery Disease conditions, Giri et al. [14], have used heart signals rather than cardiac signals to design a methodology. The heart signals, are associated with several frequency sub bands using Discrete Wavelet Transform (DWT). Hence, to retrieve the heart signal from DWT and also to reduce the dimensions of data, three statistical methods are applied to the set of DWT coefficients. Further, the normalized information is passed through a set of four classifiers such as Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Gaussian Mixture Model (GMM), and K-Nearest Neighbor (KNN) for decision making. The results indicate that the GMM model coupled with ICA provides higher accuracy rate. Further, to improve the classification performance of the Coronary Artery Disease, Babaoglu et al., [15] have developed a BPSO, GA and SVM based classification model based on stress testing data. In the proposed model, BPSO and GA techniques are applied for obtaining the relevant set of features to predict Coronary Artery Disease. Whereas, SVM is adopted as a classifier system to classify patients with CAD. For enhancing the diagnostic rate, 10 cross fold techniques are incorporated in SVM classifier. Results indicate that the proposed classification model achieves higher accuracy rate and having less complexity. To investigate the characteristics of diabetic patients and also to predict type-2 diabetic effectively, Patil et al., [16] have developed a hybrid prediction model for diabetic disease. In the proposed prediction model k-means clustering algorithm is applied to validate the class labels of diabetic dataset and the classical C4.5 method is selected to build the final classifier using k-cross fold validation method. From results, it is stated that the proposed system attains higher sensitivity and specificity rate than the other methods being compared. An intelligent hepatitis diagnostic system for caring and treatment of hepatitis patients is reported in [17]. Another application of SVM technique is also reported for effective diagnosis of cancer affected patients [18]. Ucar et al. [19], have proposed a new hybrid machine learning method for identification of tuberculosis disease. The proposed machine learning method is the combination of adaptive neuro fuzzy inference system and Rough sets. The results indicate that the proposed method provides more vibal results in comparison to other algorithms being compared. To predict the heart valve disorder, Uguz has developed an adaptive neuro fuzzy based system [20]. In the proposed system, three layers are presented for feature extraction, feature selection and classification. Feature extraction is done by DWT, whereas, the Shannon entropy algorithm is adopted for feature selection. Finally, all selected features are classified using ANFIS classifier. The proposed system obtains 98.3 % classification accuracy. For the effective diagnosis of coronary artery disease (CAD), Muthukaruppan et al.[21], have presented particle swarm optimization (PSO)-based fuzzy expert system. In the proposed system, the initial decision tree is implemented to find the best features for better prediction of coronary artery disease. Further, these features are converted into fuzzy if then rules and make a database of fuzzy rules. Prior to convert the crisp set into fuzzy set, a fuzzy membership function is applied in fuzzy set theory and this function has significant impact on the fuzzy output. Further, in this work, authors are adopted PSO algorithm to tune the value of the fuzzy membership function. From results, it is noted that the proposed fuzzy system is more capable of diagnosis of coronary artery disease in comparison to other methods being compared. Seera and Lim have described a fuzzy min-max neural network for medical data classification task [22]. The proposed system comprises of Fuzzy Min–Max neural network, Regression Tree, and the Random Forest algorithms. The efficacy of the proposed system is tested for Breast Cancer, Diabetes and Liver Disorders diseases and provides significant results. To achieve better accuracy and diagnostic rate of breast cancer affected patients, Ubeyli et al. [23], have proposed an adaptive neuro-fuzzy based inference system (ANFIS) for breast cancer detection. In this work, the first order Sugeno model is applied with two fuzzy if-then rules and further, this model integrates with neural network architecture for effective detection of breast cancer disease. It is found that the ANFIS achieves 99.08% accuracy rate.Kumar et al., have developed a rule based classification model to predict the different types of liver diseases and claimed that decision tree based classification model gives higher accuracy [24]. In continuation of their work, five machine learning approaches are applied to evaluate seminal quality and it is observed that PSO-SVM approach provides better results than MLP, DT, NB and SVM approaches [25]. Yadav et al. [26], have applied three popular machine learning approaches such as tree, statistical and SVM classifiers to find Parkinson’s disease affected patients. It is seen that the performance of logistic regression classifier is better than other classifiers.