• Monika Krishna Muddana
• Nalla Bharath
• Neeli Sri Krishna Sai Teja
• Nutakki Teja Kumar
• Lalam Jyothi Swaroop

The increase in health issues made people to look at the trending technologies like Internet of Things, cloud computing, big data, and artificial intelligence, together with modern biotechnology [14] to deal with them. So, people started investing in hospitals where they can get good treatment. With the technologies growth, the healthcare results are promising and that led to the reduction in healthcare costs. In this paper, author uses Explainable Artificial Intelligence (XAI) which is an extension of artificial intelligence that extends the capability of ai by explaining why it has given that prediction whether we can rely on its decision or not. In this paper, the author aims to predict the health care records of Cardiovascular disease patients using Explainable Artificial Intelligence (XAI) with What-if tool for getting better accuracy. It compares models and predicts the best model and justifies why it has chosen that model based on what features will also be mentioned. The author uses methods like Random Forest, Decision Tree and XGBoost. The author uses the dataset that is downloaded from the Kaggle website which consists of 70000 datapoints. Each datapoint consists of 13 features. Based on those features, model is designed and it predict the model that gives better accuracy. Based on the results, Random Forest gives an accuracy of 75% which is best compared to other models.

Cardiovascular disease, Decision Trees, Explainable Artificial Intelligence, Random Forest, XGBoost

"Explainable Artificial Intelligence to Predict Cardiovascular Diseases", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b711-b716, June-2021, Available :http://www.jetir.org/papers/JETIR2106241.pdf

"Explainable Artificial Intelligence to Predict Cardiovascular Diseases", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb711-b716, June-2021, Available at : http://www.jetir.org/papers/JETIR2106241.pdf