Using Data Visualization to Analyze the Correlation of Heart Disease Triggers and Using Machine Learning to Predict Heart Disease

With the technology improvement, the reasons cause heart disease are getting clearer. Regarding the different causes of heart disease, analyzing what causes heart disease has become mainstream nowadays. After an in-depth understanding of data analysis and machine learning-related knowledge, data analysis and data training are carried out on a dataset containing 14 columns of features. First, Python is used to visualize and analyze data. And then train test split is used to divide the data into the training set and the learning set. At last, three methods including logistic regression, decision tree classifier, and random forest classifier are used to train the data and observe which method gets the best effect. This article mainly uses numpy, matplotlib, pandas, seaborn and scikit-learn libraries in Python language for data analysis and processing.