## 5 Experiment and Result  
We conducted our experiment with R programming language using RStudio (Version 1.0.153--(c)2009-2017 RStudio, Inc.). The pre-processing stage is carried out to establish a structured database in Linux Mint environment installed on a virtual machine. Java Eclipse Integrated Development Environment provides the facility to implement the MapReduce algorithm. We collect two different sets of patient data for our simulation. We use a dataset of fetal cardiotocogram (CTG) for measuring the fetal heart rate and uterine contraction. This dataset is considered with a less hidden value which has 21 features with 3000 patient observations, and after pre-processing there were 2126 patients. The CTG dataset contains a categorical outcome in three categories: Normal, Suspect, and Pathologic. The diabetic's dataset represents the more hidden state, and dissimilarity among the attributes with eight features are used to identify the binary outcome with 1000 patient's observation (after pre-processing there were 768 patients) which predicts whether a patient has diabetes or not. We toured various hospitals in Dhaka, Bangladesh for the data. We have monitored some patients to produce data using certain IoT equipment and our datasets are gathered from an organization called Data Analysis and Technical Assistance Limited (DATA), which offers data for public use.  
At the beginning of the simulation, the packages and library files are initialized for the smooth experiment. The dataset is divided into training and test data with a probability of 0.7 and 0.3, respectively. We apply several machine-learning algorithms on both datasets and get different accuracy for a different algorithm. The multi-dimensional scaling plot is shown in Figure 3 to understand the similarity of the outcome of both datasets. This plot indicates that data are more scattered for diabetic patients than for the cardiotocogram patient. To identify the relationship between features, we calculate the correlation coefficient of data. We get a positive correlation among features of both datasets. We incorporated the genetic algorithm to eliminate the irrelevant features. In the genetic algorithm, we use the default mutation and crossover probability which is 0.1 and 0.8, respectively. After extracting features, we apply different classifiers such as random forest, support vector machine, artificial neural network, C5.0, and Naive Bayes algorithm to predict a patient disease. We also apply those classifiers before we implement the genetic algorithm to identify the best performance. In diabetic dataset classification, 0 denotes the patient who does not have diabetics, and 1 indicates the diabetic patient, whereas in CTG, 1 indicates regular patient, 2 indicates the suspect and 3 represents the pathologic. A comparison is shown in Tables 1 and 2. Evaluation criteria for diabetic patient data describes the prediction evaluation of all classifiers for the diabetic dataset before and after applying the genetic algorithm.  
Figure 3: Multi-dimensional scaling plot for both datasets.  
Figure 5: Model performance with ROC curve for Diabetes prediction. (**a**) ROC and AUC before Genetic Algorithm. (**b**) ROC and AUC after Genetic Algorithm.  
Figure 8: Model Performance with ROC curve for CTG prediction. (**a**) Performance before Genetic Algorithm. (**b**) Performance after Genetic Algorithm.