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Tuberculosis (TB) is a deadly contagious disease and a serious global health problem. It is curable but due to its lengthy treatment process, a patient is likely to leave the treatment incomplete, leading to a more lethal, drug resistant form of disease. The World Health Organization (WHO) propagates Directly Observed Therapy Short-course (DOTS) as an effective way to stop the spread of TB in communities with a high burden. But DOTS also adds a significant burden on the financial feasibility of the program. We aim to facilitate TB programs by predicting the outcome of the treatment of a particular patient at the start of treatment so that their health workers can be utilized in a targeted and cost-effective way. The problem was modeled as a classification problem, and the outcome of treatment was predicted using state-of-art implementations of 3 machine learning algorithms. 4213 patients were evaluated, out of which 64.37% completed their treatment. Results were evaluated using 4 performance measures; accuracy, precision, sensitivity, and specificity. The models offer an improvement of more than 12% accuracy over the baseline prediction. Empirical results also revealed some insights to improve TB programs. Overall, our proposed methodology will may help teams running TB programs manage their human resources more effectively, thus saving more lives.

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The accurate prediction of Web navigation patterns has immense commercial value as the Web evolves into a primary medium for marketing and sales for many businesses. Often these predictions are based on complex temporal models of users' behavior learned from historical data. Such an approach, however, is not readily understandable by business people and hence less likely to be used. In this paper, we consider several key and practical Web navigation patterns and present Bayesian models for their learning and prediction. The navigation patterns considered include pages (or page categories) visited in first N positions, type of visit (short or long), and rank of page categories visited in first N positions. The patterns are learned and predicted for specific users, time slots, and user-time slot combinations. We employ Bayes rule and Markov chain in our learning and prediction models. The focus is on accuracy and simplicity rather than modeling the complex Web user behavior. We evaluate our models on four weeks of Web navigation data. Prediction models are learned from the first three weeks of data and the predictions are tested on last week's data. The results confirm the high accuracy and good efficiency of our models.

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