Developing mHealth Solutions for Natural Family Planning

Natural Family Planning (NFP) is a method to help couples determine the fertile and infertile times of a woman’s menstrual cycle with natural indicators of fertility. NFP methods have advantages over other methods of family planning. Proper use of NFP methods also ensures high effectiveness (close to 98%) in helping couples avoid pregnancy. However, very few physicians prescribe NFP to their patients due to lack of credibility to the fertility methods and lack of access to NFP knowledge. The Marquette University College of Nursing Institute for Natural Family Planning has been researching for many years to increase knowledge and efficiency of NFP. Their proposed evidencebased Marquette Model (MM) for NFP already showed success as an internet based charting system. It is obvious to have an effective mHealth (mobile health) solution for NFP because of enormous growth of smart phones. We have designed and developed muFertility, a mHealth framework that follows the MM so that couples can chart the menstrual cycles. In this thesis, we have discussed the major human computer interface (HCI) and design issues. We also have also presented how user feedback cycle based approach can be used to incorporate user experiences in the development and deployment of a mHealth solution

Combining Machine Learning Classifiers for Stock Trading with Effective Feature Extraction

The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalized scheme. This paper intends to discuss our machine learning model, which can make a significant amount of profit in the US stock market by performing live trading in the Quantopian platform while using resources free of cost. Our top approach was to use ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to decide whether to go long or short on a particular stock. Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit. Finally, our work showcased that mixtures of weighted classifiers perform better than any individual predictor about making trading decisions in the stock market

e-ESAS: Evolution of a Participatory Design-based Solution for Breast Cancer (BC) Patients in Rural Bangladesh

Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients’ side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS—the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses several challenges faced by patients and doctors and positively impact their lives