• Pooja A
• Dr. Smitha Shekar B
• Dr. Harish G

In today’s world, diabetes and hypertension are the most common and non-communicable growing diseases in several countries. Researchers are working on early prediction and prevention of diabetes and hypertension. Several machine learning techniques are being utilized to predict both diseases. Most of the people suffering from these diseases, have higher complications related to heart disease, stroke, kidney failure, neuropathy, retinopathy, foot damage and pregnancy related issues. In this paper we have worked on diabetes and hypertension and proposed a disease prediction model to predict and classify the disease based on individual’s risk factor. Various Machine Learning algorithms like Logistic Regression, Support Vector Machine, Naïve Bayes, Multi-Layer Perceptron, k-nearest neighbours, Decision Tree, AdaBoost, Gradient Boosting, Random Forest are used for prediction of diabetes and hypertension. The results obtained after a comparative study, showed that Logistic Regression obtained high accuracy of 83.16% on diabetes dataset and Decision Tree and Gradient Boosting obtained most effective accuracy of 100% on hypertension dataset. In the proposed work, a web application is developed to provide a practical application of the disease prediction model by gathering risk factor data and a health care Chatbot provides real time assistance to patients.

Diabetes, Hypertension, Prediction, Machine Learning Algorithm, Web Application, Chatbot.

"A Machine Learning Model for Prediction of Diabetes and Hypertension Using Ensemble Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.d97-d109, September-2021, Available :http://www.jetir.org/papers/JETIR2109310.pdf

"A Machine Learning Model for Prediction of Diabetes and Hypertension Using Ensemble Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppd97-d109, September-2021, Available at : http://www.jetir.org/papers/JETIR2109310.pdf