What Is the Optimal Machine Learning Algorithm for Predicting Heart Disease?

Simple Overview:

Utilizing sophisticated algorithms, I have created a resilient model for anticipating the occurrence of heart disease, utilizing an extensive range of features. Each individual is distinctly identified by a 'patient_id,' and the model takes into account variables such as age, gender, and diverse cardiac measurements.

Analyzed Features:

Slope of Peak Exercise ST Segment:

• Assesses the adequacy of blood flow to the heart.

Thal:

• Outcome of the Thallium stress test indicating measurements of blood flow.

Resting Blood Pressure:

• Records the patient's blood pressure in a state of rest.

Chest Pain Type:

• Categorizes chest pain into distinct types, assigning numerical values for rating.

Number of Major Vessels:

• Indicates the quantity of major vessels identified through fluoroscopy.

Fasting Blood Sugar Level:

• Determines whether the patient's fasting blood sugar level exceeds 120 mg/dL.

Resting EKG Results:

• Evaluates the results of resting electrocardiography.

Serum Cholesterol Level:

• Quantifies the amount of cholesterol present in the serum.

Oldpeak Eq ST Depression:

• Quantifies ST depression induced by exercise relative to the resting state.

Gender:

• '0' designates Female, '1' designates Male.

Age:

• Denotes the age of patients in years.

Max Heart Rate Achieved:

• Represents the maximum heart rate achieved by a patient.

Exercise-Induced Angina:

• Indicates the presence of chest pain induced by exercise.

Outcome: