Using C-support vector classification to forecast dengue fever epidemics in Taiwan

Rahmawati, Dini; Huang, Yo-Ping

doi:10.1109/icsse.2016.7551552

Cited by 25 publications

(26 citation statements)

References 5 publications

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“…It is a supervised learning methodology that has three vital splitting conditions of data [5]. The information gain is the most important condition among the three.…”

Section: Decision Treementioning

confidence: 99%

Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

Karuppan,

Nithya,

Ondimuthu

2020

IJEAT

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A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

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“…It is a supervised learning methodology that has three vital splitting conditions of data [5]. The information gain is the most important condition among the three.…”

Section: Decision Treementioning

confidence: 99%

Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

Karuppan,

Nithya,

Ondimuthu

2020

IJEAT

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“…On the basis of the high accuracies obtained [21,59], we selected Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes to evaluate the factors using WEKA version 3.8.0 [60]. We used the cross-validation (10-fold) technique to evaluate the models.…”

Section: Prediction Using Machine Learning Modelsmentioning

confidence: 99%

“…In the current study, important climatic risk factors, such as temperature, relative humidity and rainfall amount, were examined. The current accuracy for prediction systems based on climate factors ranges from 82.39% to 90.5% [16,[20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2020

Preprint

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Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

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“…A number of studies related to machine learning modelling of infectious disease have been conducted on unsupervised analysis of several infectious diseases (PeterIdowu et al, 2013),measles outbreak prediction (Liao et al, 2017), dengue outbreak prediction (Rahmawati & Huang, 2016) and dengue infection risk (Fathima & Hundewale, 2012). Our prior work found that decision tree and Naive Bayes are among the techniques commonly used for disease risk prediction (Ahmad et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Handling Imbalanced Class Problem of Measles InfectionRisk Prediction Model

Ahmad

Ghani²,

Drus³

2019

IJEAT

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Measles is an emerging infectious disease with increasing number of reported cases. It is a vaccine-preventable disease;thus, it is common to have imbalanced class problem in the dataset. This study aims to resolve the imbalanced class problem for the prediction of measles infection risk and to compare the predictive results on a balanced dataset based on three machine learningtechniques. The data that was utilized in this study contained 37,884 records of suspected measles casesthat were highly imbalanced towards negative measles cases. The Synthetic Minority Over-Sampling Technique (SMOTE) was performed to balance thedistribution of the target attribute. The balanced dataset was then modelled using logistic regression, decision tree and Naïve Bayes. The predicted results indicated that logistic regression executed on the balanced dataset by SMOTE has the highest and most accurateclassification with 94.5% overall accuracy, 93.9% true positive rate, 5.8% false positive rate and 5.1% false negative rate. Therefore, SMOTE and other over-sampling approaches may be applicable to overcome imbalanced class issues in the medical dataset.

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Using C-support vector classification to forecast dengue fever epidemics in Taiwan

Cited by 25 publications

References 5 publications

Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Handling Imbalanced Class Problem of Measles InfectionRisk Prediction Model

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