ABSTRACT
Osteoporosis has recently been acknowledged as a major public health issue in developed countries because of the decrease in the quality of life of the affected person and the increase in public costs due to complete or partial physical disability. The aim of this study was to use the J48 algorithm as a classification task for data from women exhibiting changes in bone densitometry. The study population included all patients treated at the diagnostic center for bone densitometry since 2010. Census sample data collection was conducted as all elements of the population were included in the sample. The service in question provides care to patients via the Brazilian Unified Health System and private plans. The results of the classification task were analyzed using the J48 algorithm, and among the dichotomized variables associated with a diagnosis of osteoporosis, the mean accuracy was 74.0 (95% confidence interval [CI], 61.0-68.0) and the mean area under the curve of the receiver operating characteristic (ROC) curve was 0.65 (95% CI, 0.64-0.66), with a mean sensitivity of 76.0 (95% CI, 76.0-76.0) and a mean specificity of 48.0 (95% CI, 46.0-49.0). The analyzed results showed higher values of sensitivity, accuracy, and curve of the ROC area in experiments conducted with individuals with osteoporosis. Most of the generated rules were consistent with the literature, and the few differences might serve as hypotheses for further studies.
Subject(s)
Data Mining , Osteoporosis/diagnosis , Risk Assessment/methods , Aged , Algorithms , Brazil , Cross-Sectional Studies , Female , Humans , Middle Aged , Osteoporosis/diagnostic imaging , Quality of Life , Risk Factors , Sensitivity and SpecificityABSTRACT
This paper presents an evaluation of the accuracy of the Bayesian classifiers: Bayes Net, Naive Bayes and Averaged One-Dependence Estimator, to support diagnoses of osteopenia and osteoporosis. All classifiers showed good results, thus, given data, it is possible to produce a reasonably accurate estimate of the diagnosis.
Subject(s)
Algorithms , Bayes Theorem , Decision Support Systems, Clinical , Diagnosis, Computer-Assisted/methods , Machine Learning , Osteoporosis/diagnosis , Female , Humans , Reproducibility of Results , Sensitivity and SpecificityABSTRACT
This research aimed to compare the performance of two models of load balancing (Proportional and Autotuned algorithms) of the JPPF platform in the processing of data mining from a database with osteoporosis and osteopenia. When performing the analysis of execution times, it was observed that the Proportional algorithm performed better in all cases.
Subject(s)
Algorithms , Computer Communication Networks/statistics & numerical data , Electronic Health Records/statistics & numerical data , Information Storage and Retrieval/statistics & numerical data , Osteoporosis/epidemiology , Population Surveillance/methods , Age Distribution , Brazil/epidemiology , Female , Humans , Osteoporosis/diagnosis , Prevalence , Risk FactorsABSTRACT
Using the framework for developing parallel applications Java Parallel Programming Framework were conducted performance analysis of an application for the clustering data by the method of fuzzy logic combined with Gustafson-Kessel algorithm. In addition to running in a distributed environment, for comparative purposes, were also conducted collections of processing time in environments with a single Personal Computer approach. With the results obtained by collecting time of application, there was a statistical analysis to validate the application and the algorithm as well as the use of computational clustering as a way to increase performance applications.
