Fuzzy association rule mining and classification for the prediction of malaria in South Korea

Abstract

BackgroundMalaria is the world’s most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.MethodsWe describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as LOW, MEDIUM or HIGH, where these classes are defined as a total of 0–2, 3–16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak.ResultsModel accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7–8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the MEDIUM class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3.ConclusionsA previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict LOW, MEDIUM or HIGH cases 7–8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa

“…We assembled our full Ibadan dataset, denoted by D, by aggregating data for each month from January 1996 to December 2017 (22 years), creating thus a total of 264 (22 × 12) entries (Table 1), each containing the following 15 variables (Table 2) Malaria screening. Malaria parasites (MPs) were detected and counted by microscopy following Giemsa staining of thick and thin blood films [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]27 . The criterion for declaring a participant to be malaria parasitefree was no detectable parasites in 100 high-power (100×) fields in both thick and thin films.…”

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

“…Thenmozhi and Deepika [110] classified and predicted heart diseases based on a different attribute selection measures, such as information gain, gain ratio, gini index, and distance measure. Buczak et al [111] classified and predicted malaria in South Korea through extracting relationships between epidemiological, meteorological, climatic, and socio-economic data. Subasi et al [112] diagnosed chronic kidney disease, achieving the near-optimal performances on the identification of this illness subject.…”

“…Heart and vascular diseases [180] Classification RF [96,97] Classification SVM [110,114] Classification ID3 [115] Classification KNN [126][127][128] Classification Naïve Bayes [142] Classification Bayesian Networks [148] Regression Linear regression [181,182] Classification DL [183] Regression Gradient boosting [184] Classification KNN + RF + DT Hepatic diseases [99] Classification SVM [113] Classification ID3 [185] Regression Linear regression [115] Classification KNN [129,185] Classification Naïve Bayes [186] Classification Ensemble Feature Selection [170] Classification Cross-sectional models Infectious diseases [78,82] Clustering K-means Clustering [85] Clustering DBSCAN [72,98,[101][102][103][104][105] Classification SVM [107,111] Classification ID3 [72,121,123] Classification KNN [133] Classification Naïve Bayes [71,[147][148][149]…”

Discrimination and Prediction of Traffic Congestion States of Urban Road Network Based on Spatio-Temporal Correlation

“…Based on the basic parameters such as the running speed, flow and density of the upstream and downstream traffic flow of the detection section, the input layer, the middle layer and the output layer of BP neural network were constructed to process the data and fit the parameters to determine the road traffic congestion states. Buczak al et al [12] proposed a new algorithm to distinguish traffic congestion based on the analysis of the fluctuation of road traffic flow parameters. Based on the analysis of a large number of traffic flow road occupancy data, the deviation of cumulative occupancy between upstream and downstream observation stations is calculated.…”