REMOTE DIAGNOSIS BASED ON SYMPTOMS

Computer Science and Engineering

Machine Learning Approach for distinctive Disease Prediction victimisation Machine Learning is based on prediction modelling that predicts illness of the patients per the symptoms provided by the users as an input to the system. This paper provides a thought of predicting multiple diseases victimisation Machine Learning algorithms. Here we are going to use the idea of supervised Machine Learning during which implementation are done by applying Decision Tree, Random Forest, Naïve Bayes and KNN algorithms which can facilitate in early prediction of diseases accurately and higher patients care. The results ensured that the system would be useful and user oriented for patients for timely diagnoses of diseases in a patient. Medicine and health care are a number of the foremost crucial elements of the economy and human life. There’s an incredible quantity of change within the world we tend to live in currently and also the world that existed many weeks back. Everything has turned gruesome and divergent. During this state of affairs, wherever everything has turned virtual, the doctors and nurses are putting up most efforts to save lots of people's lives even though they need to danger their own. There are still some remote villages that lack medical facilities. Machines are forever considered better than humans as, with none human error, they will perform tasks more expeditiously and with an even level of accuracy. A disease predictor is known as virtual doctor, which might predict the sickness of any patient with none human error. Also, in conditions like COVID-19 and EBOLA, a disease predictor is a blessing because it will determine a human's sickness with none physical contact

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