V NAÏVE BAYES Naive Bayes is a classification technique based on Bayes Theorem with an assumption of independence among predictors In simple a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature A Naive Bayesian model is easy to build with no muddled iterative parameter estimation which makes it particularly useful for very large datasets Bayesian classifiers are the measurable classifiers Bayesian classifiers can foresee class membership probabilities such as the probability that a given tuple belongs to a particular class Bayes theorem provides a way of calculating posterior probability P c x from P c P x and P x c Look at the equation below P c x is the posterior probability of class c target given predictor x attributes P c is the earlier likelihood of class P x c is the likelihood which is the probability of predictor given class P x is the prior probability of predictor VI LR Logistic regression is named for the function used at the core of the method the logistic function The logistic function also called the sigmoid function was developed by statisticians to describe properties of population growth in ecology rising rapidly and maxing out at the carrying capacity of the environment It s an
S shaped curve that can take any real valued number and map it into a value between 0 and 1 but never exactly at those limits 1 1 e value Where e is the base of the natural logarithms Euler s number or the EXP function in your spreadsheet and value is the actual numerical value that you want to transform Following is a plot of the numbers between 5 and 5 transformed into the range 0 and 1 using the logistic function given below FIG 4 Logistic Function REPRESENTATION USED FOR LOGISTIC REGRESSION Logistic regression uses an equation as the representation very much like linear regression Input values x are joined linearly using weights or coefficient values referred to as the Greek capital letter Beta to predict an output value y A key variance from linear regression is that the output value being modeled is a binary values 0 or 1 rather than a numeric value Below is an example logistic regression equation y e b0 b1 x 1 e b0 b1 x Where y is the predicted output b0 is the bias and b1 is the coefficient for the single input value x Every column in your input data has an associated b coefficient a constant real value that must be learned from your training data The real representation of the model that you would store in memory or in a file are the coefficients in the equation the beta value or b s VII PERFORMANCE METRICS
To analyze the performance of the techniques twoapproaches were used First one is the pixel based and second is the image based approach All pixels encount to a candidate that partially or totally overlaps a manually segmented shiny lesion were known as True Positive TP All candidate pixels outside this approach were considered as False Positives FP All exudate pixels that were not segmented by this method treated as False Negatives FN Researchers evaluate their performance in terms of sensitivity accuracy specificity Error Rate and PPV The difficulty that comes in evaluating the specificity is that if all image pixels are encountered the number of true Negative TN pixels will large as compared to FP values The performance can be evaluated and analysed on the basis of various parameters used in this research work Various parameters are Sensitivity Sensitivity is also known as true positive rate that measures the proportion of positives that are correctly identified Higher the value of sensitivity better results can be obtained It can be calculated as Sensitivity TP TP FN Error Rate It is defmed as the rate at which errors occur in a transmission system Lower the value of Error Rate better will be the result It can be calculated as Error Rate 1 Accuracy Accuracy Accuracy can be defined as the degree of closeness of measurements of a quantity to that quantity s true value Higher value of accuracy better will be the results It can be calculated as Accuracy TP TN TP FP FN TN Specificity Specificity is also known as true negative rate which measures the proportion of negatives that are correctly identified Higher the value of specificity better results can be obtained It can be calculated as Specificity TN TN FP processing it involves brightness correction edge detection intensity adjustment Histogram equalization etc VIII CONCLUSION The system is designed to predict stages of blindness a diabetic patient can go through i e depending upon the severity of blindness According to the stages of retina harmed medicine are suggested And depending upon the prescription other diseases can also be predicted
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