• Mrs.ALEESHA MARIA SIBY
• Mrs.SHIVAPRIYA B
• Mrs.THABITHA IGNATIOUS FERNANDEZ
• Mrs. TINA ELSA VARGHESE
• Ms. JYOLSNA MARY P

Malaria is one of the significant general medical issues in India. Early expectation of a Malaria is the key for control of disease grimness, mortality just as decreasing the danger of transmission of disease in the network and can help policymakers, well-being suppliers, restorative officers, service of well-being and other well-being associations to all the more likely target therapeutic assets to regions of most prominent need. In this work, we have designed doctor-patient information portal with several parameters. Machine learning algorithms like random forest and support vector machine are used to train the data. Information of has been gathered from various resources. Parameters utilized are cold, rigor, fatigue, headache, bitter_tounge and etc. A substantial quantities of tests were gathered from various sources. It is seen that execution of the model created utilizing Random Forest is more precise than Support Vector Machine(SVM). The Random Forest model can foresee the episode 15 - 20 days ahead of time. Anyway precision of forecast can be expanded utilizing additionally preparing information. This model can be scaled-up at nation level.

Malaria, Machine learning, Random forest

"A Patient-doctor portal for Malaria parasite prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.314-318, May 2019, Available :http://www.jetir.org/papers/JETIRCV06065.pdf

"A Patient-doctor portal for Malaria parasite prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp314-318, May 2019, Available at : http://www.jetir.org/papers/JETIRCV06065.pdf