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This research paper aims to give an in-depth analysis of the healthcare field and data analysis related to healthcare. The Healthcare industry usually generates numerous amounts of data. These data are used for making a decision, so this must be very accurate. In order to identify the errors in the healthcare data, Exploratory Data Analysis (EDA) is proposed in this research. The EDA tries to detect the mistake, find the perfect data, check the errors, and determine the correlation. The most dependent analytical techniques and tools for improving the healthcare performance in the areas of operations, decision making, prediction of disease, etc. In most situations, a complicated combination of pathological and clinical evidence is used to diagnose cardiac disease. Because of this complication, clinical practitioners and scientists are keen to learn more about how to anticipate cardiac disease efficiently and accurately. With the use of the K-means algorithm, the factors that cause heart-related disorders and problems are considered and forecasted in this study. The research is based on publicly available medical information about heart disease. There are 208 entries in this dataset, each with eight characteristics: the patient's age, type of chest discomfort, blood glucose level, BP level, heart rate, ECG, and so on. The K-means grouping technique as well as visualisation and analytics tools, are utilised to forecast cardiac disease. The proposed model's prediction is more accurate than the other model, according to the results.
How to Cite
Heart disease, cardiac arrest, chest pain, EDA, K-means, data analytics
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