Common human diseases prediction using machine learning based on survey data

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others. © 2022, Institute of Advanced Engineering and Science. All rights reserved.

Prediction of Type II Diabetes Using Machine Learning Approaches

Diabetes is a major threat all over the world. It is rapidly getting worse day by day. It is found that about 90% of people are affected by type 2 diabetes. Now, in this COVID-19 epidemic there is a terrible situation worldwide. In this situation, it is very risk to go hospital and check diabetes properly. In this era of technology, several machine learning techniques are utilized to evolve the software to predict diabetes more accurately so that doctors can give patients proper advice and medicine in time, which can decrease the risk of death. In this work, we tried to find an efficient model based on symptoms so that people can easily understand that they have diabetes or not and they can follow a proper food habit which can reduce the risk of health. Here, we implement four different machine learning algorithms: Decision tree, Naïve Bayes, Random Forest, and K-Nearest Neighbor. After comparing the performance by using different parameter, the experimental results showed that Naïve Bayes algorithm performed better than other algorithms. We find the highest 90.27% accuracy from Naïve Bayes algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.