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Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication
and technology. These techniques can help to save the life of millions in the medical assisted environment and can
be utilized in health-care system where health expertise is less available. Fast food consumption increased from the
past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other
organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular
diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more
death than any other disease globally. The objective of this research is to analyze the available data pertaining to
cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset
of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then
pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The
analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive
based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision
and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random
Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is
developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of
available medical sensors to predict cardiovascular diseases on a real-time basis
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