Heart attack is a myocardial necrosis caused by acute and persistent ischemia and hypoxia of coronary artery which manifestations are arrhythmia, shock or heart failure, which can be fatal. A heart attack occurs when the flow of blood to the heart is blocked. The blockage is most often a buildup of fat, cholesterol and other substances, which form a plaque in the arteries that feed the heart (coronary arteries). Sometimes, a plaque can rupture and form a clot that blocks blood flow.
The dataset consists of 303 rows and 14 columns with label Output. Data contains categorical as well as numerical data.
There is only one row that is duplicated which can easily be dropped.
Data Visualization is done by step by step process with critical analysis. With the help of python libaries such as matplotlib and seaborn, I plotted barplots, distribution plots and line plots to show the comparision and correlation between features.
To explain and identify the problem and resolve medical objectives, different data science techniques, which interpret the medical goals, have been implemented to diagnose heart disease. A suitable machine learning algorithm called Logistic Regression is preferred for the training and implementation in python for developing and evolving the predictive model. This algorithm executed on the model will help medical experts to predict and diagnose heart attacks in the patient dataset. Exploratory Data Analysis is performed using python libraries such as Matplotlib and Seaborn to gain insights from the data.
The dataset is trained and tested using the algorithm Logistic Regression which gave an accuracy of 81.5%.