Update README.md

README.md

@@ -201,3 +201,40 @@ Analyze the severity level of the patient considering about all the data that
 gathered via     the question answering system. According to the severity level
 patient will be advised what he/she can do as the next step and if its possible
 we suppose to give medical recommendation.  
+
+**IT18125962 Nimantha W.A.R**
+Covid-19 has become a major lung-related disease daily with the current epidemic.
+Therefore, it is essential to identify patients as early as possible and to 
+distinguish Covid-19 patients from other patients with lung-related diseases 
+such as pneumonia. The main problem with doing this is the time it takes to get 
+the preliminary results from the existing test methods. Although there are 
+methods available for fast counter results call PCR. 
+Most of people go backward to do PCR test. Because they tell it is pain full and
+it is risk to spread corona. Therefore, people requests more accurate
+things faster than PCR test.
+Chest X-rays are a cheap and fast method used to diagnose lung-related diseases 
+such as pneumonia. This research focuses on the possibility of using X-rays and 
+CT scan of the chest and machine learning model use to identify Covid-19 and 
+distinguish them from other selected lung-related diseases using symptoms.
+
+**Objectives**
+Training three model and select model that have highest accuracy to predict risk
+level and classify the covid-19 and covid-pneumonia using patient’s symptoms and
+patient’s age and sex within this year. 
+
+
+* To develop model for classify covid-19 and pneumonia   
+
+In this train machine learning model to classify covid-19 or pneumonia. 
+
+
+* To improve accuracy of trained algorithm.  
+
+Algorithm should be trained and improved accuracy until come to expected accuracy. 
+
+
+* To develop user friendly mobile application   
+
+In this objective develop a mobile application to collect symptoms and upload it
+to the cloud for do the prediction and classification through the Machine 
+learning model.