Title: Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning,
Abstract: We build a deep reinforcement learning (RL) agent that can predict the
likelihood of an individual testing positive for malaria by asking questions
about their household. The RL agent learns to determine which survey question
to ask next and when to stop to make a prediction about their likelihood of
malaria based on their responses hitherto. The agent incurs a small penalty for
each question asked, and a large reward/penalty for making the correct/wrong
prediction; it thus has to learn to balance the length of the survey with the
accuracy of its final predictions. Our RL agent is a Deep Q-network that learns
a policy directly from the responses to the questions, with an action defined
for each possible survey question and for each possible prediction class. We
focus on Kenya, where malaria is a massive health burden, and train the RL
agent on a dataset of 6481 households from the Kenya Malaria Indicator Survey
2015. To investigate the importance of having survey questions be adaptive to
responses, we compare our RL agent to a supervised learning (SL) baseline that
fixes its set of survey questions a priori. We evaluate on prediction accuracy
and on the number of survey questions asked on a holdout set and find that the
RL agent is able to predict with 80% accuracy, using only 2.5 questions on
average. In addition, the RL agent learns to survey adaptively to responses and
is able to match the SL baseline in prediction accuracy while significantly
reducing survey length.