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This study aims to (modeling to identify the signs and) developed a model to predict the risk of severe fluoride is toxic to the bone in the elderly. And analyze the factors that affect the occurrence of fluoride toxicity to bone. Using logistic regression analysis. The data is analyzed by the retention of the International Health Programme. By collecting data, behavioral health, water consumption. And assessment of skeletal and muscle pain. Of seniors in the district Talatkhwan. Choeng Doi Suthep, Chiang Mai, and a total of 50 people and 30 people respectively
analysis using multivariate logistic regression group (Multinomail logistic regression) showed that the level of fluoride in urine. Affect the classification of symptoms of fluoride poisoning the bones in the elderly. At 0.05 and the equation is the equation logit 2 logit_1 = 2.025 - 2.708 (volume fluoride. More than 1 but less than 2) - 3.114 (Volume fluoride. More than 2), logit_2 = 1.609 - 2.526 (volume fluoride. More than 1 but less than 2) - 2.862 (Volume fluoride. More than 2) and logit_3 = 0 (due logit_3 at this level is a Phase 2 and 3 as reference category is set to 0) by the equation describing the variation in the levels symptoms of fluoride is. toxic to the bone marrow, only 24.8 percent (〖R_CS〗 ^ 2 = 0.248), and the prediction was correct 57.5 percent from the analysis using equation logit Initially, it was found that the equation can predict the onset of only 2 levels. The symptoms of fluoride poisoning and symptoms of fluoride poisoning in Phase 2 or 3 only because the symptoms in Phase 1 did not show obvious signs, resulting in difficult to diagnose. Were analyzed by means of logistic regression bis (Binary logistic regression) to generate a classification of people with symptoms of fluoride toxicity to bone and asymptomatic. Factors that influence the occurrence of fluoride is toxic to bone. Amount of fluoride in urine as well as the analysis of the symptoms. The significance level of 0.05 and a model to predict the log (odds) = -0.222 + 1.667 (the fluoride in the urine), where the model can explain the variation in the occurrence of fluoride poisoning. In the elderly, only 12.3 percent (〖R_CS〗 ^ 2 = 0.123), and the prediction was correct 71.3 percent when done to determine the suitability of the model using the Hosmer and Lemeshow found that the model is appropriate (. x ^ 2 = 1.233, p-value = 0.873) at the 0.05 significance level.
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