Predicting and diagnosing heart disease is the biggest challenge in the medical industry and relies on factors such
as the physical examination,symptoms and signs of the patient.
Factors that influence heart disease are body cholesterol levels, smoking habit and obesity, family history of illnesses,
blood pressure, and work environment. Machine learning algorithms play an essential and precise role in the prediction of heart disease.
Advances in technology allow machine language to combine with Big Data tools to manage unstructured and exponentially growing data.
Heart disease is seen as the world’s deadliest disease of human life. In particular, in this type of disease,
the heart is not able to push the required amount of blood to the remaining organs of the human body to perform regular functions.
Heart disease can be predicted based on various symptoms such as age, gender, heart rate, etc. and reduces the death rate of heart patients.
Due to the increasing use of technology and data collection, we can now predict heart disease using machine learning algorithms.
Now let’s go further with the task of heart disease prediction using machine learning with Python.
Predict whether a patient should be diagnosed with Heart Disease. This is a binary outcome.
Positive (+) = 1, patient diagnosed with Heart Disease
Negative (-) = 0, patient not diagnosed with Heart Disease
Experiment with various Classification Models & see which yields greatest accuracy.
Examine trends & correlations within our data
Determine which features are most important to Positive/Negative Heart Disease diagnosis