Common Human Disease Prediction Using Machine Learning Based on Survey

Abstract

One of the main drivers of today’s AI revolution is machine learning and data mining. The development of machine learning with huge data and the improvement of data mining sorting type results have already changed the Artificial Intelligence industry. So, as time is going on this field of machine learning and data mining is getting improved in the medical treatment sector. We are finding various problems and it will be solved to improve it later. In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, multiple techniques have been developed to overcome the constraints of the disease approach. In the world, there are eleven types of diseases - Covid 19, Normal Flue, Migraine, Heart Disease, Lung Disease, Kidney Disease, Stomach Disease, Gastric, Diabetics, Bone Disease, Autism are the very Common diseases at this time. In this analysis, we looked at disease predictions and the factors that influence them. We studied a range of symptoms and took a survey from people in order to complete the task. This proposed work predicts the individual’s symptoms and recognizes the disease. Several classification algorithms have been employed to train the model. This paper also presents a comparable examination by analyzing the enforcement of different types of machine learning algorithms. Furthermore, the performance of the model is measured with the help of performance evaluation matrices. So, from all the outputs of proposed classifier implementations, it shows that Random Forest algorithms have achieved the maximum precision of 88.2% compared to the other classifications. Finally, we discovered that the Part classifier surpasses the others.