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\title{Clinical Dengue Data Analysis and Prediction using Multiple Classifiers: An Ensemble Techniques}
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\begin{document}

             \author[1]{Veena Kumari H  M}

             \author[2]{Dr. uresh D  S}

             \author[3]{Dr. Dananjaya P  E}

             \affil[1]{  Channabasaveshwara Institute of Technology, Gubbi,Tumkur}

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\date{\small \em Received: 1 January 1970 Accepted: 1 January 1970 Published: 1 January 1970}

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\begin{abstract}
        


The dengue infection is caused by the mosquito Aedes aegypti. According to WHO, 50 to 100 million dengue infections will occur every year. Data-miming techniques will extract information from the raw data. Dengue symptoms are fever, severe headache, body pain, vomiting, diarrhea, cough, pain in abdomen etc. The research work is carried out on real data and the patient data is collected from the Department of General Medicine, PESIMSR, Kuppam, Andrapradesh. Dataset consists of 18 attributes and one target value. Research work has been done on binary classification to classify dengue positive (DF) and dengue negative (NDF) cases using different ML techniques. The proposed work demonstrates that ensemble techniques bagging, boosting and stacking gives better results than other models. The Extreme Gradient Boost (XGB), Random Forest by majority voting and stacking with different meta classifiers are the ensemble techniques used for the binary classification. The dataset is divided into 80% training and 20 % testing dataset. Performance parameters used for the analysis are accuracy, precision, recall, and f1 score, and compared the proposed model with other ML models. The experimental results shows that the accuracy of extended boost, random forest and stacking is 98%, 99%, 99% for training dataset and 97%, 94%,98%testing dataset respectively. The extended metrics ROC, Precision -Recall curve and AUC better analysis

\end{abstract}


\keywords{dengue fever, aedes aegypti, XGB, stacking, ROC, AUC.}

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\section[{I. Introduction}]{I. Introduction}\par
engue fever (DF) is an arthropod-borne viral disease common past three decades. According to WHO, 51-101 million new infections with dengue occur every year in more than a hundred endemic countries \hyperref[b0]{[1]}. Dengue fever is a severe viral infection with potentially fatal consequences. Dengue fever was originally known as "water poison." The dengue caused by the female Aedes aegypti mosquito is shown in Fig.  {\ref 1} A Female Aedes Aegypti Mosquito\par
In the 1780s, the first clinically recognized epidemics of dengue occurred at the same time in Africa, Asia, and North America. Benjamin Rush was named "break-bone fever" based on the features of arthralgia and myalgia. The dengue epidemic was first reported in Chennai in 1780, the first virologically proven outbreak of dengue fever in India appeared at Calcutta and the East Coast of India in 1963-64. In the 1970s and 1980s, epidemic activity accelerated dramatically, resulting in the widespread of viruses and mosquito vectors and the consequent DENV transmission across the world \hyperref[b1]{[2]}. The first major DHF epidemic occurred in the Philippines during 1953-1954, continued by a rapid global spread of DF/DHF epidemics. The first major DHF/DSS epidemics in India occurred in 1996, at Delhi and Lucknow, and later extended throughout the country. In India outbreaks of dengue have become more common in many parts. Between 2010 to 2014 incidence of reported cases of dengue was 34.81 per million population. Dengue fever became endemic in Orissa, Uttarakhand, Bihar, Assam, and Jharkhand, in 2010 \hyperref[b2]{[3]}.  
\section[{II. Background Study}]{II. Background Study}\par
Kassaye Yitbarek Yigzaw et al \hyperref[b1]{[2]} presented a benchmarking platform for the prediction of communicable diseases. Rathi et al \hyperref[b3]{[4]} studied dengue infection in Rajasthan. The study was based on 100 admitted children and he classified the patients based on their symptoms. Kalayanarooj S \hyperref[b2]{[3]} demonstrates the clinical appearances of dengue and DHF. Aldallal, A.S \hyperref[b4]{[5]} explained that data mining techniques are used for the prediction of non-communicable diseases like heart and diabetes. Agrawal et al \hyperref[b6]{[7]} demonstrated the ensemble approach by using multiple classifiers Ada boost, and a decision tree for the prediction of diabetes. Ghosh et al [10] used multiple classifiers for the sentiment analysis performance assessment. Gupta et al \hyperref[b12]{[12]} compared different ML approaches for heart disease prediction. Mesafint et al \hyperref[b14]{[14]} explained ML algorithms for the prediction of HIV/AIDS tests. 
\section[{III. Proposed Methodology}]{III. Proposed Methodology}\par
The ensemble models are Extreme Gradient Boost (XGB), Random Forest (RF) by majority voting, and Stacking, which is based on a combination of heterogeneous classifiers like NB, KNN, and SVM. It is very helpful to consider ensemble techniques \hyperref[b5]{[6]}, for dengue fever diagnosis and prediction. The proposed framework is shown in Fig \hyperref[fig_1]{3}. The main aim of data acquisition and the data pre-processing module is to get the Dengue fever dataset and process them into a suitable form for further analysis. Datasets have features/attributes which will finally distinguish the data into patient sick and healthy. The dataset has thirty-eight features and different data types. The dataset is spitted into an 80\% training set and a 20\% testing dataset. The pre-processing includes feature selection and missing value imputation \hyperref[b7]{[8]}. The proposed model combines different classifiers such as Naïve Bayes, K -Nearest Neighbor, and Support vector machine. For each classifier, the output is predicted.\par
Each base classifier is used in the ensemble framework by training data to make it useful for the prediction of dengue. Dataset features and target values are known to each classifier, which in turn can predict whether the disease is present or not. 
\section[{i. Description of the Dengue Dataset}]{i. Description of the Dengue Dataset}\par
The patient data is collected from the Department of General Medicine, PESIMSR, Kuppam, Andrapradesh. The patient is diagnosed in the laboratory using the dengue duo card test shown in fig 4. Dataset consists of 18 attributes and one target value.   The number of patients having each symptom is listed in Table \hyperref[tab_0]{I} and corresponding bar charts explain the importance of each feature \hyperref[b9]{[9]} are shown in fig.  {\ref 7}. Among 140 dengue-infected cases all the patients are suffering from fever,106 headache, 97 and 94 myalgia and arthralgia and 83 low back pain and others.  
\section[{ii. XGBoost}]{ii. XGBoost}\par
Boosting is a broadly used and highly effective machine learning algorithm. An end-to-end tree boosting system called XGBoost is widely used by data experts. The important factor is its scalability for better accuracy. The system is ten times faster than existing conventional methods. The scalability of XGBoost is due to several algorithm optimizations. Parallel and distributed computing will make learning faster \hyperref[b15]{[15]}. In the stacking algorithm, the base (first-level) classifiers are trained by the same set of the training sample, which is used to prepare the inputs for the meta (second-level) classifier, which may cause overfitting. The stackingCVclassifier uses the cross-validation method. The dataset is split into k folds, and k-1 folds are used to fit the level-1 classifier in k successive rounds. In every iteration, the level-1 classifiers are then applied to the remaining subset. The predictions of the base classifiers are then stacked and which is an input to the level-2 classifier. 
\section[{NO. OF PATIENTS}]{NO. OF PATIENTS} 
\section[{IV. Performance Evaluation}]{IV. Performance Evaluation}\par
The clinical dengue fever data set was used to analyse the performance of the ensemble model and to compare it with the other models. The class labels dengue infected (DF) with the dengue not infected (NDF) is replaced with class 1 and class 0 to maintain uniformity \hyperref[b16]{[16]}. Each dataset is split into training and testing sets. Cross validations of 10-fold are applied. performance measure of each base classifier, as well as the ensemble model, is calculated using a confusion matrix. The base classifiers NB, SVM \& KNN are trained first and then they are tested. The proposed research work analysed the performance of the ensemble methods XGB, RF, and Stacking. The metrics are accuracy, recall, precision, and f1-score. The confusion matrix illustrates the actual and predicted classification \hyperref[b15]{[15,}\hyperref[b17]{17]}. The equations (  {\ref 1}), (  {\ref 2}), (3), and (  {\ref 4}), are used to calculate the metrics \hyperref[b17]{[17]}.    \hyperref[tab_3]{III} and Fig.  {\ref 11}. The ensemble methods XGB, RF, and Stacking give 98.57\%, 99.12\%, and 99.56\% for the training dataset, whereas 97.80\%, 94.82\% and 98.27\% for the testing dataset. We observed better accuracy for ensemble methods.     \hyperref[tab_4]{IV}. The AUC for the proposed ensemble XGB is 97.14\% and 97.81\% for random forest 98.14\% and 99.14\%, for stacking 98.14\% and 98.68\% for testing and Training datasets respectively. As shown in Table \hyperref[tab_3]{III}, the AUC values for the datasets lie between 0.97 to 0.99, indicating that the positive class values are correctly distinguished from the negative class values. 
\section[{Table II: Confusion Matrix}]{Table II: Confusion Matrix} 
\section[{Actual}]{Actual} 
\section[{Table V: Auc Comparision}]{Table V: Auc Comparision} 
\section[{V. Conclusion}]{V. Conclusion}\par
The main objective of this research work is to the prediction of dengue fever using ensemble techniques. We used bagging, boosting, and stacking methods for prediction and the end results are compared with the NB, KNN, and SVM models. The experimental results prove that Ensemble techniques are the best models for the prediction of dengue fever. The techniques were analysed using performance metrics. The accuracy for the extended boost, random forest with majority voting, and stacking using metaclassifiers gives better accuracy for both the training and testing datasets compared to other models. The extended analysis was done by using the roc curve and precision-recall curve, which explains the performance of the models. The Area under the curve lies between 0.97 to 0.99. The ensemble models are the better models for the prediction of dengue-infected patients. \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 2 :}\end{figure}
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\noindent\textbf{3}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Fig. 3 :}\end{figure}
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\noindent\textbf{4}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 4 :}\end{figure}
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\noindent\textbf{5}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 5 :}\end{figure}
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\noindent\textbf{6}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Fig. 6 :}\end{figure}
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\noindent\textbf{89}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Fig. 8 :Fig. 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-8.png}
\caption{\label{fig_6}}\end{figure}
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\noindent\textbf{1011}\includegraphics[]{image-9.png}
\caption{\label{fig_7}Fig. 10 :Fig. 11 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{13}\includegraphics[]{image-10.png}
\caption{\label{fig_8}Fig. 13 :}\end{figure}
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\noindent\textbf{14}\includegraphics[]{image-11.png}
\caption{\label{fig_9}Fig. 14 :}\end{figure}
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\noindent\textbf{1617}\includegraphics[]{image-12.png}
\caption{\label{fig_10}Fig. 16 :Fig. 17 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-13.png}
\caption{\label{figure13}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-14.png}
\caption{\label{figure14}}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-15.png}
\caption{\label{figure15}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-16.png}
\caption{\label{figure16}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{I} \par 
\begin{longtable}{P{0.6978344562078922\textwidth}P{0.074446583253128\textwidth}P{0.06626564003849857\textwidth}P{0.01145332050048123\textwidth}}
\tabcellsep \multicolumn{2}{l}{Target}\\
200\tabcellsep \tabcellsep \\
150\tabcellsep \tabcellsep \\
100 50\tabcellsep \tabcellsep \tabcellsep Year 2022\\
\tabcellsep \tabcellsep Non Dengue\\
\tabcellsep \multicolumn{2}{l}{Fig. 7: Bar Chart Representation}\\
b) Ensemble Methods\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Clinical Feature Ensemble means combining multiple models. This approach gives better performance compared to a}\tabcellsep No. of Patients\\
\multicolumn{2}{l}{Fever single model. Thus, a set of models is used for Headache predictions than a single model [7]. The main challenge is to obtain a base model which gives different kinds of}\tabcellsep 140 106\tabcellsep ( ) D\\
\multicolumn{2}{l}{Myalgia errors. If the ensemble technique of bagging, boosting,}\tabcellsep 97\\
\multicolumn{2}{l}{Arthralgia and stacking are used for classification, high accuracies}\tabcellsep 94\\
\multicolumn{2}{l}{Low Backache can be obtained. Bagging creates a different subset of}\tabcellsep 83\\
\multicolumn{2}{l}{Retro Orb Pain training data from the sample training dataset \& the final Rashes output depends on majority voting. e.g., Random Vomiting Forest. Boosting the creation of sequential models by Pain Abdomen combining weak learners with strong learners and the finally constructed model has the highest accuracy e.g.,}\tabcellsep 71 65 57 41\\
XGBOOST and ADA BOOST\tabcellsep Bleeding\tabcellsep 39\\
i.\tabcellsep Cough\tabcellsep 30\\
\tabcellsep Diarrhea\tabcellsep 25\\
\tabcellsep Sore Throat\tabcellsep 16\\
\tabcellsep Breathlessnes\tabcellsep 6\\
\tabcellsep Seizures\tabcellsep 5\\
\tabcellsep \tabcellsep © 2022 Global Journals\end{longtable} \par
 
\caption{\label{tab_0}Table I :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.37141982864137085\textwidth}P{0.024969400244798042\textwidth}P{0.023929008567931457\textwidth}P{0.00832313341493268\textwidth}P{0.36517747858017136\textwidth}P{0.023929008567931457\textwidth}P{0.023929008567931457\textwidth}P{0.00832313341493268\textwidth}}
SVM RF\tabcellsep \multicolumn{2}{l}{Training Dataset}\tabcellsep \tabcellsep SVM RF\tabcellsep \multicolumn{2}{l}{Testing dataset}\tabcellsep \\
NB\tabcellsep \tabcellsep \tabcellsep \tabcellsep NB\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Accuracy of Random Forest: 99.12}\tabcellsep \multicolumn{3}{l}{Accuracy of Random Forest :}\tabcellsep \\
\multicolumn{4}{l}{Accuracy of Naive Bayes model: 95.40 precision recall f1-score 0 0.93 0.98 0.9 1 0.98 0.92 0.95 Accuracy of Support Vector Classifier: 97.5 precision precision recall f1-score recall f1-score 0 0.96 0.98 0.97 1 0.99 1.00 1.00 0 0.98 1.00 0.99 1 1.00 0.98 0.99}\tabcellsep \multicolumn{4}{l}{Accuracy of Naivey bayes : 93.17 precision recall f1-score 0 0.94 0.98 0.96 1 0.97 0.93 0.95 94.82 Accuracy of Support Vector machine: precision recall f1-score 89.65 precision recall f1-score 0 0.91 0.98 0 0.97 0.98 0.97 0.94 1 0.98 0.89 0.93 1 0.98 0.96 0.}\\
XGB\tabcellsep \tabcellsep \tabcellsep \tabcellsep XGB\tabcellsep \tabcellsep \tabcellsep \\
KNN\tabcellsep \tabcellsep \tabcellsep \tabcellsep KNN\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Accuracy of Extreme Gradient Boost}\tabcellsep \multicolumn{4}{l}{Accuracy of Extreme gradient Boost}\\
\multicolumn{2}{l}{:98.57 precision}\tabcellsep \multicolumn{2}{l}{recall f1-score}\tabcellsep \multicolumn{2}{l}{:97.80 precision}\tabcellsep \multicolumn{2}{l}{recall f1-score}\\
0\tabcellsep 0.99\tabcellsep 0.97\tabcellsep 0.98\tabcellsep 0\tabcellsep 0.97\tabcellsep 0.98\tabcellsep 0.97\\
1\tabcellsep 0.97\tabcellsep 0.99\tabcellsep 0.98\tabcellsep 1\tabcellsep 0.98\tabcellsep 0.96\tabcellsep 0.97\end{longtable} \par
 
\caption{\label{tab_1}Accuracy of K-Neighbors Classifier :96.49 precision recall f1-score 0 0.93 1.00 0.97 1 1.00 0.93 0.96 Accuracy of K-Nearest Neighbour : 85.66 precision recall f1-score 0 0.86 0.97 0.91 1 0.96 0.81 0.88}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: Matrix and Experimental Results of Training and Testing Dataset of the Ensemble and Other M Models]} 
\caption{\label{tab_2}97 Stacking Accuracy of Stacking CV Classifier :99.56 precision recall f1-score 0 1.00 0.99 1.00 1 0.99 1.00 1.00 Stacking Accuracy of Stacking CV Classifier: 98.27 precision recall f1-score 0 0.97 0.99 0.98 1 0.99 0.96 0.98 Confusion}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{III} \par 
\begin{longtable}{P{0.16894409937888197\textwidth}P{0.24285714285714283\textwidth}P{0.4381987577639751\textwidth}}
\tabcellsep \tabcellsep Year 2022\\
\tabcellsep \tabcellsep 47\\
\tabcellsep \tabcellsep ( ) D\\
Classifiers\tabcellsep Training Dataset\tabcellsep Testing Dataset\\
NB\tabcellsep 95.40\tabcellsep 93.17\\
KNN\tabcellsep 96.49\tabcellsep 85.66\\
SVM\tabcellsep 97.51\tabcellsep 89.65\\
XGB\tabcellsep 98.57\tabcellsep 97.80\\
RF\tabcellsep 99.12\tabcellsep 94.82\\
Stacking\tabcellsep 99.56\tabcellsep 98.27\\
\tabcellsep \tabcellsep © 2022 Global Journals\end{longtable} \par
  {\small\itshape [Note: Global Journal of Computer Science and TechnologyVolume XXII Issue II Version I]} 
\caption{\label{tab_3}Table III :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{IV} \par 
\begin{longtable}{P{0.1296383647798742\textwidth}P{0.09221698113207548\textwidth}P{0.23121069182389936\textwidth}P{0.07885220125786163\textwidth}P{0.004009433962264151\textwidth}P{0.054795597484276724\textwidth}P{0.0026729559748427676\textwidth}P{0.13632075471698113\textwidth}P{0.058805031446540874\textwidth}P{0.06147798742138364\textwidth}}
\tabcellsep \tabcellsep 110\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep 90\tabcellsep \multicolumn{2}{l}{97.5}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep 80\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep NB\tabcellsep KNN\tabcellsep SVM\tabcellsep XGB\tabcellsep RF\tabcellsep \\
Year 2022\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
48\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XXII Issue II Version I ( ) D Global Journal of Computer Science and Technology\tabcellsep Classifiers NB KNN SVM RF XGB Ensemble Stacking\tabcellsep Training dataset Precision Recall (\%) (\%) NDF 93 98 DF 98 92 NDF 93 100 DF 100 93 NDF 96 98 DF 99 100 NDF 98 100 DF 100 98 NDF 99 97 DF 97 99 NDF 100 99 DF 99 100\tabcellsep f1-score (\%) 95 95 97 96 97 100 99 99 98 98 100 100\tabcellsep \tabcellsep \multicolumn{2}{l}{Classifiers NB KNN SVM RF XGB Ensemble}\tabcellsep Testing Dataset Precision (\%) NDF 94 DF 97 NDF 86 DF 96 NDF 91 DF 98 NDF 97 DF 98 NDF 97 DF 98 NDF 97\tabcellsep Recall (\%) 98 93 97 81 98 89 98 96 98 96 99\tabcellsep f1-score (\%) 96 95 91 88 94 93 97 97 97 97 98\\
\tabcellsep © 2022 Global Journals\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table IV :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.5241106719367589\textwidth}P{0.15118577075098813\textwidth}P{0.17470355731225296\textwidth}}
Year 2022\tabcellsep \tabcellsep \\
50\tabcellsep \tabcellsep \\
( ) D\tabcellsep \tabcellsep \\
Classifier\tabcellsep Testing Dataset\tabcellsep Training Dataset\\
Auc\textunderscore Nb\tabcellsep 0.9629\tabcellsep 0.9514\\
Auc\textunderscore Knn\tabcellsep 0.8333\tabcellsep 0.9342\\
Auc\textunderscore Svc\tabcellsep 0.9444\tabcellsep 0.9956\\
Auc\textunderscore Xgb\tabcellsep \tabcellsep 0.9781\\
Auc\textunderscore Rf\tabcellsep 0.9814\tabcellsep 0.9914\\
Auc\textunderscore Scv\tabcellsep 0.9814\tabcellsep 0.9868\end{longtable} \par
 
\caption{\label{tab_5}}\end{figure}
 		 		\backmatter   			 
\subsection[{Acknowledgment}]{Acknowledgment}\par
Our sincere thanks to Dr. Veerapuram Manoj Reddy, Department of General Medicine, PES Medical sciences and Research, Kuppam, Andrapradesh for his support for the collection of dengue data. 			  			  				\begin{bibitemlist}{1}
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