Medical_Analysis_Project

The dataset and assignments come from web site : "freeCodeCamp.org"

The dataset description :

File "medical_examination.csv" contains a data with medical examinations. Rows in the dataset represents patients and columns represents inforamtions from examined patients.

Feature	Variable Type	Variable	Value Type
Age	Objective Feature	age	int (days)
Height	Objective Feature	height	int (cm)
Weight	Objective Feature	weight	float (kg)
Gender	Objective Feature	gender	categorical code
Systolic blood pressure	Examination Feature	ap_hi	int
Diastolic blood pressure	Examination Feature	ap_lo	int
Cholesterol	Examination Feature	cholesterol	1: normal, 2: above normal, 3: well above normal
Glucose	Examination Feature	gluc	1: normal, 2: above normal, 3: well above normal
Smoking	Subjective Feature	smoke	binary
Alcohol intake	Subjective Feature	alco	binary
Physical activity	Subjective Feature	active	binary
Presence or absence of cardiovascular disease	Target Variable	cardio	binary

Code :

A full code with comments is located in a file "medical_analysis.py". Python with its libraries like numpy, matpotlib and others helped in making charts and calcuations needed for this analysis and conclusions.

Charts of analysis :

Bar plot to see differences between patients with and without cardiovascular disease:

• patients who have a cardiovascular disease have cholesterol and glucose levels above normal
• patients who have a cardiovascular disease are a little less physically active

Heatmap correlation matrix to see positive or negative correlations between features:

• features : 'weight', 'ap-lo ', 'cholesterol', 'gluc' and 'BMI', 'age ' have low but maybe significant correlation with 'cardio'.

Machine Learning Model:

Using conclusions from an analysis, a classifier model predicts if patient has or hasn't a cardiovascular disease with 72% accuracy: Full code in file 'ML_model_classifier.py'

• doing another column that should help with a model training and then scaling features 'BMI' column that uses columns 'weight' and 'height' #bmi[i] = df['weight'][i] / pow(df["height"][i] * 0.01, 2)

• checking if classes are equal and no need of over/under sampling

• Using Logistic regression and a feature selection with RFE

  Result:

  -seeing columns that can be dropped and overall infos about features

• Result of training and testing of a model:

Accuracy of Logistic Regression test: 0.72