Multiple Disease Prediction Using Different Machine Learning Algorithms Comparatively
Amol A. Nirmal 1 , Palash M. Madakuntanwar2,Nikhil K. Khemnar3,Sahil S. Patel 4. Prof.Poonam Bhawke 5.
Student, Department of Technology, Savitribai Phule University, Pune, India1,2,3,4.
Assistant Professor, Department of Technology, Savitribai Phule university, Pune, India5.
ABSTRACT:
Using Machine learning, our project proposes disease prediction system. For small issues, the users need to go in person to the hospital for check-up that is longer intense. Also handling the telecom entails appointments is kind of agitated. Such a tangle may be solved by Disease prediction application by giving correct steerage relating to healthy living. Over the past decade, the utilization of the particular disease prediction tools alongside the regarding health has been magnified because of a range of diseases and fewer doctor-patient magnitude relation. Thus, during this system, we have a tendency to area unit concentrating on providing immediate and correct disease prediction to the users concerning the symptoms they enter alongside the severity of disease expected. Best appropriate rule and doctor consultation are given during this project. For prediction of diseases, totally different machine learning algorithms area unit wont to guarantee fast and correct predictions. In one channel, the symptoms entered are crosschecked with the information. Further, can be preserved within the information if the symptom is new that its primary work is and therefore the different channel will offer severity of disease expected. A web/android application is deployed for user for straightforward immovableness, configuring and having the ability to access remotely wherever doctors cannot reach simply. usually users don't seem to be privy to all the treatment relating to the actual disease, this project additionally appearance forward to providing medication and drug consultation of disease expected. Therefore, this arrangement helps in easier health management.
Keywords: Machine Learning, KNN algorithm, SVM, Decision Tree Algorithm, Naïve Bayes Algorithm, Django, Python.
