Implementing WEKA for medical data classification and early disease prediction

Kumar, Narander; Khatri, Sabita

doi:10.1109/ciact.2017.7977277

Cited by 45 publications

(23 citation statements)

References 24 publications

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“…RMSE is used to measure the difference between the expected and the observed values from the environment that is being modeled [51]. The RMSE values can be used to distinguish model performance in a training period with that of a validation period as well as to compare the individual model performance to that of other predictive models.…”

Section: Root Mean Square Error (Rmse)mentioning

confidence: 99%

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Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Mohapatra¹,

Tripathi²,

Pal³

et al. 2020

Preprint

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Background: India has a rising rate of malaria as well as a high mortality rate despite awareness and efforts being focused on the issue. Some regions are profoundly affected than others, such as in Odisha, where the prevalence of malaria is nearly a third of the whole country. This study investigated the influence of climate factors on the incidence of malaria in the Sundargarh district in the state of Odisha, India. Methods: Block-wise observed station rainfall data was sourced from the Special Relief Commissioners' (SRC) web portal. Gridded surface maximum temperature and relative humidity data were accessed from the European Center for Medium-range Weather Forecast (ECMWF) reanalysis data archive. Malaria incident data were collected from the Directorate of Public Health, Government of Odisha. WEKA machine learning tool with two classifier techniques, Multi-Layer Perceptron (MLP) and J48 with 10-fold cross-validation, percentile split (66%), and supplied test options, were used for the Malaria prediction. A comparative analysis was carried out on both techniques to ascertain the superior model amongst the two, concerning the prediction accuracy of malaria in the context of a varying climate. Classifier accuracy, Root Mean Square Error (RMSE), Kappa, and ROC scores were the indicators used for the analysis. Results: The results suggested that J48 had exhibited a better skill to MLP and illustrated less error with a positive kappa. In particular, the 10-fold cross-validation method had better performance over the percentile Spilt (66%) and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Further, seasonal temperature and humidity variation had shown a better association with malaria incidents in comparison to rainfall.Conclusion: The performance of the machine learning methods for Sundargarh was particularly better during the monsoon and post-monsoon when the events are at the peak. The results were encouraging for the utilization of climate forecast for prediction of malaria incidences. It is thus recommended that the J48 classifier machine learning technique could be adopted for the development of malaria early warning system.

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Section: Root Mean Square Error (Rmse)mentioning

confidence: 99%

“…ROC is a curve that characterizes the randomly chosen probability of positive instance over negative instances [51]. It is a measure of the skill of different classifiers with the true positives (TP) to the false-positive rates (FPR).…”

Section: Receiver Operating Characteristics (Roc)mentioning

confidence: 99%

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Mohapatra¹,

Tripathi²,

Pal³

et al. 2020

Preprint

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show abstract

“…Narandar Kumar et. al [1], has conducted the experiment on weka tool using five classifier models like J48,K-nearest neighbor, ,Random Forest, Support vector machine and Naive Bayes on 24 attribute dataset.The experimental outcome showed that Random Forest achieved higher prediction accuracy compared to other models. Haya Alasker et.…”

Section: Related Workmentioning

confidence: 99%

Chronic Disease Prediction using Effective Feature Selection

Saurabh¹,

Nargis²

2019

IJRTE

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Abstract: Healthcare is a major sector where there is demand for predictive analytics using machine learning. Healthcare will be largely benefited when useful knowledge can be transferred into timely action to manage hazardous situations in medical sector. Chronic kidney disease is a life threatening disease which can be prevented with timely right predictions and appropriate precautionary measures. In this paper, various machine learning classifiers are applied on the medical dataset to develop a prediction model to tell if a person's present medical condition can lead to the chronic stage of the disease in future. The higher prediction accuracy and decreased build time is obtained with reduced feature set attributes by applying Best First and Greedy stepwise algorithm combined with different classification techniques like Naive Bayes ,Support vector machine (SVM), J48, Random Forest, and K Nearest Neighbor(KNN).

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“…In paper [8], it is shown how the different classifiers are used for the classification purpose using the tool Weka. Here they have considered predicting the disease in an early stage.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Test on Classification Algorithms

2019

ijrte

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Nowadays, a huge amount of data is generated due to the growth in the technologies. There are different tools used to view this massive amount of data, and these tools contain different data mining techniques which can be applied for the obtained data sets. Classification is required to extract useful information or to predict the result from these enormous amounts of data. For this purpose, there are different classification algorithms. In this paper, we have compared Naive Bayes, K*, and random forest classification algorithm using Weka tool. To analyze the performance of these three algorithms we have considered three data sets. They are diabetes, supermarket and weather data set. In this work, an analysis is made based on the confusion matrix and different performance measures like RMSE, MAE, ROC, etc

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Implementing WEKA for medical data classification and early disease prediction

Cited by 45 publications

References 24 publications

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Comparative Analysis of Machine Learning Classifiers for the Prediction of Malaria Incidence Attributed to Climatic Factors

Chronic Disease Prediction using Effective Feature Selection

Performance Test on Classification Algorithms

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