Application of Machine Learning Technique for Disease Cluster Detection and Functional Characterization

Human disease have been made disclose with the development of the biomolecular process. It isvaluable in prevention, diagnosis and treatment. It reveals common functional changes that accompany aparticular disease and recommend treatment that can be proper for the disease. This inadequacy can bepotentially overcome by looking for common biological processes rather than only specified gene matchesbetween diseases. In this work we uses the three types of disease heart disease, diabetes disease andCOVID disease. The use of connection between biological processes to estimate disease similarity couldenhance the identification and characterization of disease similarity. In this work we maintain a databasewere we store the number of records which we make the matches to the trained dataset .Machine learningmake a contribution were we can predict the result in a low cost. In this project we are using the twoalgorithm linear regression for comparison and random forest algorithm which helps to predict thedetection of disease and get the output. Here we take the parameters for predicting and make the futureresult of disease. It is beneficial to make a usage of this particular attribute to save a time and it’s a lowcost. We can say this is one of the best algorithms to predict the disease detection as we are getting 51%of accuracy.