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Journal: International Journal of Advanced Research in Engineering and Technology (IJARET) (Vol.12, No. 03)

Publication Date:

Authors : ; ;

Page : 673-683

Keywords : Viral Disease; Machine Learning; Anti-viral Medicine; Naïve Bayes; Decision Tree; Random Forest;

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The combat between viral diseases and the human has been continuing since long history. But according to the evolution theory every entity of the world works for their survival always so, even the minute viruses also. Therefore, spreading infection through viruses are rapidly-evolving day by day and therefore it imposes a substantial burden on humans in terms of morbidity and mortality. Even though in this era, we have many advance techniques for diagnosis, prevention and treatment of infectious diseases, still the arrival of new diseases put a critical and emergence challenge to the global population. Recent example is the novel coronavirus, COVID-19, which was first found in Wuhan, China, and promptly became a global pandemic. There was no medicine available to cure the patient from this novel virus disease. In this tough situation, doctors and drug specialist were manually recommending the existing medicine based on the symptom occurs in the patients. During this process lots of infected people have died due to a lack of proper medicine. Therefore, in this work we have implemented a disease prediction system based on various symptoms of the disease. In addition to this, we came with an idea which can help the medicine industries towards the development of medicine for any viral disease using Machine Learning technique. Basically, this technique analyses the symptoms and predicts the best suitable medicine for any new disease. Moreover, this method, also predict the required composition of chemical elements that can be used by the medicine companies medicine to develop the new medicine under the supervisions of drug experts.

Last modified: 2021-04-03 15:50:20