Abstract: Technology has aided the improvement of individual health, healthcare, biomedical research as well as public health. Therefore, healthcare institutions are seeking to develop integrated information-management environments to consolidate the inevitable application of big data to health care. There exist various entry points into the medical world where computational tools assist patient care matters; reporting results of tests, allowing direct entry of orders or patient information by clinicians, facilitating access to transcribed reports, and in some cases supporting telemedicine applications, because of disorganized and incomplete patient records pose an obstacle to patient care. The most common medium by which records of medical history are kept is paper making data management a severe impediment to productivity. However, the promise of a more efficient healthcare service is obvious through the use of automated health records management systems. Heart disease is a common disease that is overlooked by most. In this study, we discuss how a person can figure out if they need to go to a doctor for a health check-up for any heart-related issues using machine learning algorithms. Keywords: Data Science, Statistics, Python, Data mining, Machine learning, Analytics, Big Data, Disease Prediction, Firebase, Supervised Learning, Unsupervised Learning, ElectrocardioGram(ECG).