Cardiovascular disease continues to be one of the leading causes of death globally, demonstrating the critical role of efficient and reliable prediction models. Here in this study a dataset that is integrated from five heart disease datasets originated from publicly available sources such as UCI for Heart Attack risk prediction and analysis with 1,888 instances are used. Fourteen primary factors that include age,abnormality of cholesterol level, type of chest pain, as well as exercise related parameters from demographic and clinical dimensions were investigated in order to find the association with heart disease. Significant trends were found using data visualization to show high heart risks with certain chest pain types and high maximum heart rate. A correlation matrix illustrates important inter-feature relationships and sheds new light on the predictabilitheckability of the features. This study highlights the possibility to exploit demographic and clinical information for early detection of high risk individuals and in the future, to allow medical interventions and improve health state.