Prajval Gupta
ABSTRACT
The rapid growth of applications of latest information technology into the field of medical sciences have founded the idea to develop such a platform through which pre-diagnosis of diseases could be easy, efficient and less time consuming. This paper talks about two frameworks designed using machine learning algorithms such as ANN, SVM and Decision Tree Induction to develop the models through which a number of diseases can be pre-diagnosed simultaneously with the analysis of symptoms initially recorded in the patient's body. These symptoms and physical readings have been taken as inputs to produce the output i.e. the predicted disease. The most important factors contributing for multiple disease prediction were determined such as age, sex, body temperature, blood pressure and symptoms like nausea, vomiting and fever. Data sets were collected from different hospitals in India during this research. All the models used were able to perform with an accuracy above 85%.
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Index Terms
- Human-Machine Interface System for pre-Diagnosis of Diseasesusing Machine Learning
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