The Heart Failure Prediction Dataset

Context

Heart disease is easier to treat when detected early, heart disease is the leading cause of death in the all over the world. The term “heart disease” refers to several types of heart conditions. In the Pakistan and some other countries, the most common type of heart disease is coronary artery disease (CAD), which can lead to heart attack. You can greatly reduce your risk for heart disease through lifestyle changes.
I have uploaded this dataset together to call my fellow data scientists to run their NLP algorithms and Kernels to find and explore the heart failure predictions etc. by them selves.

Content

The data contains complete history of heart patients (so data scientists from different parts of the world can work with it). The dataset is collected from Pakistan, Faisalabad hospital named institute of cardiology.

### About this dataset

• Age : Age of the patient
• Age Group: Such as 21-30 and 31-40 are grouped
• Gender : Sex of the patient
• Locality: Rural or Urban
• Marital status: Married or unmarried
• Smoking: yes/no
• Depression: yes/no
• Mortality: Died= 0 and Alive= 1
• Follow.Up: Number of visiting time
• cp : Chest Pain type chest pain type
• Diabetes: 0 means yes and 1 means no
• chol : cholestoral in mg/dl fetched via BMI sensor
• fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
• trestbps : resting blood pressure (in mm Hg)
• rest_ecg : resting electrocardiographic results
  Value 0: normal
  Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
  Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
• thalach : maximum heart rate achieved
• PLATELET_COUNT
• Hemoglobin
  and much more, see dataset

Inspiration

Here are some ideas to explore:

1. Create a model for predicting mortality caused by Heart Failure.
2. Can we compare the mortality rate with other famous old datasets and see the correlation
3. Can we compare the genders and ages with other datasets of multiple cities of Pakistan to see the resemblance
4. Can we compare the genders and ages with other datasets of multiple countries to see the resemblance

Any other ideas you can think of

I am looking forward to see your work and ideas and will keep adding more ideas to explore

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