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The rate of HIV infection in the migrant farm worker community is 10 times the national average. A survey was conducted of 106 female migrant farm workers in rural Northwest Ohio to assess HIV knowledge. The average participant's age was 28.7 years, 78 spoke Spanish, and 47 had an < or =8th- grade education. Fifty-six women received their information on HIV/AIDS from television. Eighty-seven women identified sexual contact as the major source of HIV transmission and 54 women identified the combination of sex, use of needles, and blood contact as the important routes. Sixty-nine women identified both homosexual and heterosexual intercourse as risk factors. Only 58 women identified perinatal infection as a route of HIV transmission and 59 women knew that treatment was available to prevent perinatal transmission. Although the majority of women had a good general knowledge of HIV transmission, further prevention education on perinatal transmission is needed among this population.

Abstract

Among the most prevalent and serious diseases that affect women is breast cancer. A large number of women succumb to breast cancer each year. Breast cancer must be detected in its early stage. To deal with this challenge, Deep Neural Network (DNN) is used to achieve the success. In medical science, DNN has played a vital role in the diagnosis of a wide range of illnesses. In this study, we investigate the use of Regularized Deep Neural Network (R-DNN) for the prediction of breast cancer. A variety of optimization techniques, such as Limited-memory Broyden Fletcher Goldfarb Shanno (L-BFGS), Stochastic Gradient Descant (SGD), Adaptive Moment Estimation (Adam), and activation functions like as Tanh, Sigmoid, and Rectified Linear Unit (ReLu) are used in the simulation of R-DNN. The Independent Component Analysis (ICA) approach is used to identify the most effective features to be used in the study. To measure the efficacy of the model, training and testing of the proposed network is carried out using the Wisconsin Breast Cancer (WBC) (Original) dataset from the University of California at Irvine (UCI) Machine Learning repository. The detailed analysis of the accuracy is carried out and compared to the accuracy of other author's model. We find that the proposed network attains the highest accuracy.

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