In medical imaging, Computer Aided Diagnosis (CAD) is a rapidly growing dynamic area of research. In recent years, significant attempts are made for the enhancement of computer aided diagnosis applications because errors in medical diagnostic systems can result in seriously misleading medical treatments. Machine learning is important in Computer Aided Diagnosis. After using an easy equation, objects such as organs may not be indicated accurately. So, pattern recognition fundamentally involves learning from examples. In the field of bio-medical, pattern recognition and machine learning promise the improved accuracy of perception and diagnosis of disease. They also promote the objectivity of decision-making process. For the analysis of high-dimensional and multimodal bio-medical data, machine learning offers a worthy approach for making classy and automatic algorithms.
Many researchers have worked on different machine learning algorithms for disease diagnosis. Researchers have been accepted that machine-learning algorithms work well in diagnosis of different diseases. In this survey paper diseases diagnosed by MLT are heart, diabetes, liver, dengue and hepatitis.
Iyer et al. [11] has performed a work to predict diabetes disease by using decision tree and Naive Bayes. Diseases occur when production of insulin is insufficient or there is improper use of insulin. Data set used in this work is Pima Indian diabetes data set. Various tests were performed using WEKA data mining tool. In this data-set percentage split (70:30) predict better than cross validation. J48 shows 74.8698% and 76.9565% accuracy by using Cross Validation and Percentage Split Respectively. Naive Bayes presents 79.5652% correctness by using PS. Algorithms shows highest accuracy by utilizing percentage split test.
Meta learning algorithms for diabetes disease diagnosis has been discussed by Sen and Dash [12] . The employed data set is Pima Indians diabetes that is received from UCI Machine Learning laboratory. WEKA is used for analysis. CART, Adaboost, Logiboost and grading learning algorithms are used to predict that patient has diabetes or not. Experimental results are compared on the behalf of correct or incorrect classification. CART offers 78.646% accuracy. The Adaboost obtains 77.864% exactness. Logiboost offers the correctness of 77.479%. Grading has correct classification rate of 66.406%. CART offers highest accuracy of 78.646% and misclassification Rate of 21.354%, which is smaller as compared to other techniques.
An experimental work to predict diabetes disease is done by the Kumari and Chitra [13] . Machine learning technique that is used by the scientist in this experiment is SVM. RBF kernel is used in SVM for the purpose of classification. Pima Indian diabetes data set is provided by machine learning laboratory at University of California, Irvine. MATLAB 2010a are used to conduct experiment. SVM offers 78% accuracy.
Sarwar and Sharma [14] have suggested the work on Naive Bayes to predict diabetes Type-2. Diabetes disease has 3 types. First type is Type-1 diabetes, Type-2 diabetes is the second type and third type is gestational diabetes. Type-2 diabetes comes from the growth of Insulin resistance. Data set consists of 415 cases and for purpose of variety; data are gathered from dissimilar sectors of society in India. MATLAB with SQL server is used for development of model. 95% correct prediction is achieved by Naive Bayes.
Ephzibah [15] has constructed a model for diabetes diagnosis. Proposed model joins the GA and fuzzy logic. It is used for the selection of best subset of features and also for the enhancement of classification accuracy. For experiment, dataset is picked up from UCI Machine learning laboratory that has 8 attributes and 769 cases. MATLAB is used for implementation. By using genetic algorithm only three best features/attributes are selected. These three attributes are used by fuzzy logic classifier and provide 87% accuracy. Around 50% cost is less than the original cost. Table 2provides the Comprehensive view of Machine learning Techniques for diabetes disease diagnosis.
Analysis:
Naive Bayes based system is helpful for diagnosis of Diabetes disease. Naive Bayes offers highest accuracy of 95% in 2012. The results show that this system can do good prediction with minimum error and also this technique is important to diagnose diabetes disease. But in 2015, accuracy offered by Naive Bayes is low. It presents 79.5652% or 79.57% accuracy. This proposed model for detection of Diabetes disease would require more training data for creation and testing. Figure 4shows the Accuracy graph of Algorithms for the diagnosis of Diabetes disease according to time.
Advantages and Disadvantages of Naive Bayes:
Advantages: It enhances the classification performance by eliminating the unrelated features. Its performance is good. It takes less computational time.
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Disadvantages: This algorithm needs large amount of data to attain good outcomes. It is lazy as they store entire the training examples [16] .
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