Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple features. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate prediction of disease diagnosis based on symptoms using machine learning algorithms. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, multiple machine learning algorithms including Decision Tree, Random Forest, Naive Bayes and Neural Network algorithms were used to analyze patient data based on multiple features, including age, illness history and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily, given a sufficient amount of data. Owing to a structured data arrangement, the random forest and decision tree algorithms’ prediction rate is more than 90% as compared to more complex methods such as neural networks and the naïve Bayes algorithm.
Purpose of the study: This research aims to investigate the impact of mass media exposure and women's autonomy on the use of contraceptives, along with other potential determinants at the individual level and community level among women in Pakistan. Methodology: Data was extracted from Pakistan Demographic & Health Surveys 2017-18. The sample size included 10,461 non-pregnant and married women from a total of 15,068 ever-married women. The analysis was done using two-level mixed-effects logistic regression for the binary outcome variable, i.e., current contraceptive use (yes/no). Main Findings: Significant factors positively associated with contraceptive use at the individual level were women's education, wealth index, parity, age at first cohabitation, child mortality experience, and mass media exposure. Community attributes like region (Sindh, KPK, Balochistan as compared to Punjab), residence (rural as compared to urban) had an inverse relationship with contraceptive use. At the same time, women's education and an ideal number of children were the significant positive associates. Women's empowerment has though insignificant, but it has a positive impact on the use of contraceptives at both individual and community levels. Applications of this study: Family planning programs can be extended by focusing on women residing in rural settings or in high fertility intentions communities, less educated and unprivileged younger women who had reduced the uptake of contraceptives. An increase in women's access to education, media exposure, employment, and women's empowerment can help attain Pakistan's contraceptive prevalence targets. Novelty/Originality of this study: The current study's effect of individual and community-level factors was investigated using National-level data, mainly focusing on the role of mass media and women's autonomy.
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