Random Forest and Deep Learning Performance on the Malaria DREAM Sub Challenge One

In several countries, vector-borne diseases play a significant role in the burden of public and individual health. Mosquitoes transmit diseases such as dengue and malaria. Malaria is a disease caused by Plasmodium parasites and transmitted by Anopheline mosquitoes species. In 2017, WHO estimates 200 million cases, affecting mainly children under five years old. On 2018 Mexico, reported 799 Malaria cases showing a more similar trend for 2019 so far. Among the effort to eradicate malaria, a crowd-sourced event called DREAM challenge, where participants have to produce machine learning models and strategies on biological data. Also, the baseline truth is not released during the challenge. On this DREAM challenge, the participants can use any approach any design to build a predictor to predict the clearance of and concentration of artemisin (the standard antimalarial drug). On this work, I discuss differences in my strategy over sub challenge one in which my predictions ranked among the top three performers.The journal name is "Research in Computing Science" An open access research journal on Computer science and computer engineering. The Journal does not provide a DOI neither ISBN, For the moment the journal is not indexed in WoS or EI. This is a very modest effort to promote the use on ML/AI on Mexico https://www.rcs.cic.ipn.mx/2020_149_5