RESUMEN
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.
Asunto(s)
Minería de Datos , Osteoporosis/diagnóstico , Medición de Riesgo/métodos , Anciano , Algoritmos , Brasil , Estudios Transversales , Femenino , Humanos , Persona de Mediana Edad , Osteoporosis/diagnóstico por imagen , Calidad de Vida , Factores de Riesgo , Sensibilidad y EspecificidadRESUMEN
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.
Asunto(s)
Algoritmos , Teorema de Bayes , Sistemas de Apoyo a Decisiones Clínicas , Diagnóstico por Computador/métodos , Aprendizaje Automático , Osteoporosis/diagnóstico , Femenino , Humanos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
The aim of this study was to determine the prevalence of osteopenia and osteoporosis in a female population, that had bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) in a specialized clinic in the south of Brazil. We conducted a cross-sectional study including 1,871 women that performed scans between January and December 2012. We conducted a logistic regression analysis with all independent variables and outcomes (osteopenia, osteoporosis and fracture risk). According to DXA results, 36.5% of women had normal BMD, 49.8% were diagnosed with osteopenia and 13.7% with osteoporosis. Menopause and age over 50 years old were risk factors for osteopenia and osteoporosis while prior hysterectomy and BMI greater than 25 were protective factors. For the outcome of fracture at any site the risk factors were age over 50 years old, osteopenia and osteoporosis (OR = 2.09, 95% CI:1,28-3, 40) and (OR = 2.49, 95% CI:1,65-3, 74), respectively.