berkkilicoglu / ml-modelling-disease-analysis Goto Github PK

Note: In this repository is detailed analysis based on machine learning. You can also modify and use it in different datasets and to solve different problems.

./dataset: directory contains the dataset in the format .xlsx. The dataset consists of only 2 types of classes.

Note: grid_search.py It performs hyper parameter scanning, the execution time of the code can be long depending on the given number of parameters. In the ./results directory; You can take a look at the grid search results that I have previously obtained with my own runs.

• Original Dataset: Diabetes Health Indicators Dataset

Features:

Diabetes_binary: person's diabetes status.

HighBP: High blood pressure (0:no high, 1:high)

HighChol: High Cholesterol (0:no high, 1:high)

CholCheck: Cholesterol control status (0: Didn't get it done in 5 years, 1: Got it done in 5 years)

BMI: Body mass index

Smoker: Have you smoked at least 100 cigarettes in your entire life? (0:no 1:yes)

Stroke: (Ever told) you had a stroke. (0:hayır 1:evet)

HeartDiseaseorAttack: coronary heart disease (CHD) or myocardial infarction (MI) (0:hayır 1:evet)

PhysActivity: physical activity in past 30 days - not including job (0:hayır 1:evet)

Fruits: Consume Fruit 1 or more times per day (0:hayır 1:evet)

Veggies: Consume Vegetables 1 or more times per day (0:hayır 1:evet)

HvyAlcoholConsump: Heavy alcohol consumption? (0:no 1:yes)

AnyHealthcare: Do you have any health insurance? (0:no 1:yes)

NoDocbcCost: Has there been a time in the last 12 months when you needed to see a doctor but couldn't go because of the cost? (0:no 1:yes)

GenHlth: Would you say that in general your health is: scale 1-5 1 = excellent 2 = very good 3 = good 4 = fair 5 = poor

MentHlth: Number of days with mental health problems in the last 30 days.

PhysHlth: Number of days with physical health problems in the last 30 days.

DiffWalk: Do you have serious difficulty walking or climbing stairs? 0 = no 1 = yes

Sex: Gender (0:Female, 1:Male)

Age: Age category with 13 levels (1 = 18-24, 9 = 60-64, 13 = +80 years)

Education: Education level

Income: Income level 1= 10,000$ 8= +75,000$