Heart-Failure-Using-Clinical-Record-Set

In this project, we will develop and evaluate the performance and the predictive power of a model trained and tested on data collected from using academic performance data. The list of tasks below :

1. Data Preparation and Exploration
2. Data standardization
3. Splitting the dataset into Train and Test (8:2 Combinations and use 10 –fold cross validation)
4. Predict the accuracy of classifier and evaluate them.
5. plot the graph
6. Calculate the computational complexity of the model

Source of dataset:

Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.

Dataset link: https://www.kaggle.com/andrewmvd/heart-failure-clinical-data

Dataset features: 0 age 299 non-null float64 1 anaemia 299 non-null int64
2 creatinine_phosphokinase 299 non-null int64
3 diabetes 299 non-null int64
4 ejection_fraction 299 non-null int64
5 high_blood_pressure 299 non-null int64
6 platelets 299 non-null float64 7 serum_creatinine 299 non-null float64 8 serum_sodium 299 non-null int64
9 sex 299 non-null int64
10 smoking 299 non-null int64
11 time 299 non-null int64
12 DEATH_EVENT 299 non-null int64

Data Preparation and Exploration

Sex- Gender of patient Male=1,Female=0 Age-Age of patient

Diabetes- 0=No,1=Yes

Anaemia- 0=No,1=Yes

High_blood_pressure- 0=No,1=Yes

Smoking- 0=No,1=Yes

DEATH_EVENT- 0==No,1=Yes

Data standardization

Data Standardization is a data processing workflow that converts the structure of disparate datasets into a Common Data Format. In this notebook , i have used standard scalar technique for standardization. Heat map shows correlation between different features:

Splitting the dataset into Train and Test

I have split the dataset into 80:20 with 10 fold cross validation.

Predict the accuracy of classifier and evaluate them.

Different Regression Algorithm on dataset

Different Classifiacrion Algorithm on dataset

Calculate the computational complexity of the model

For regrssion: Time complexity of different model:

for classifiaction: Time complexity of different model: