  Neglected tropical diseases (NTDs) continue to affect the livelihood of
individuals in countries in the Southeast Asia and Western Pacific region.
These diseases have been long existing and have caused devastating health
problems and economic decline to people in low- and middle-income (developing)
countries. An estimated 1.7 billion of the world's population suffer one or
more NTDs annually, this puts approximately one in five individuals at risk for
NTDs. In addition to health and social impact, NTDs inflict significant
financial burden to patients, close relatives, and are responsible for billions
of dollars lost in revenue from reduced labor productivity in developing
countries alone. There is an urgent need to better improve the control and
eradication or elimination efforts towards NTDs. This can be achieved by
utilizing machine learning tools to better the surveillance, prediction and
detection program, and combat NTDs through the discovery of new therapeutics
against these pathogens. This review surveys the current application of machine
learning tools for NTDs and the challenges to elevate the state-of-the-art of
NTDs surveillance, management, and treatment.
