Symptom-based disease prediction system using machine learning

Abstract

Healthcare has been an important industry from then till now, and it is said to be one of the sectors which plays a critical role in preventing the increasing number of a particular disease. In this era of new technology, machine learning has been used in a lot of industries, and one would be the healthcare industry. In the healthcare field, machine learning contributes significantly to predicting a disease as to simplify the process of the manual disease diagnosis and bring convenience to both the doctor and patient. In this paper, a disease prediction system will be implemented with the use of supervised learning algorithm to allow patient in identifying disease themselves based on their symptoms. Few supervised learning algorithms are being trained and tested in terms of their accuracy, and the algorithm with the highest accuracy is used for the prediction. The chosen supervised learning algorithms to be tested include Bernoulli Naïve Bayes, Decision Tree, and Support Vector Machine.