Pima Indians Diabetes Database

Predicting the onset of diabetes using machine learning approaches

The Pima Indian Diabetes Dataset, originally from the National Institute of Diabetes and Digestive and Kidney Diseases, contains information of 768 women from a population near Phoenix, Arizona, USA. The outcome tested was Diabetes, 258 tested positive and 500 tested negative. Therefore, there is one target (dependent) variable and the 8 attributes (TYNECKI, 2018): pregnancies, OGTT(Oral Glucose Tolerance Test), blood pressure, skin thickness, insulin, BMI(Body Mass Index), age, pedigree diabetes function. The Pima population has been under study by the National Institute of Diabetes and Digestive and Kidney Diseases at intervals of 2 years since 1965. As epidemiological evidence indicates that T2DM results from interaction of genetic and environmental factors, the Pima Indians Diabetes Dataset includes information about attributes that could and should be related to the onset of diabetes and its future complications.

📌 Python v 3.7

📌 Libraries used:

• pandas
• numpy
• seaborn
• matplotlib.pyplot
• sklearn
• statsmodels

📌 Jupyter notebook was used.

🔔 In case of any problem to visualise the project, please check here

📕 Dataset source: Kaggle

⭐ PS: Please do not forget to drop a star on this repo, if you like it!

REFERENCES

1. TYNECKI P. Predict diabetes diagnosis for Pima Female Indians with Logistic Regression. 2018. Available on: https://www.kaggle.com/ptynecki/pima-indians-diabetes-prediction-with-lr-84.

2. SCHULZ LO, CHAUDHARI LS. High-Risk Populations: The Pimas of Arizona and Mexico Curr Obes Rep. 2015 Mar 1; 4(1): 92–98. Available on: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4418458/

3. RUIZ-ALEJOS A, CARRILLO-LARCO RM, MIRANDA JJ, GILMAN RH, SMEETH L, BERNABE-Ortiz A. Skinfold thickness and the incidence of type 2 diabetes mellitus and hypertension: an analysis of the PERU MIGRANT study. Public Health Nutr. 2020;23(1):63-71. doi:10.1017/S1368980019001307

4. FRYAR CD, GU Q, OGDEN CL. Anthropometric reference data for children and adults: United States, 2007–2010. National Center for Health Statistics. Vital Health Stat 11(252). 2012.

5. VAN GAAL L., SCHEEN A. Weight Management in Type 2 Diabetes: Current and Emerging Approaches to Treatment, Diabetes Care 2015; 38(6): 1161 - 1172. Available on http://care.diabetesjournals.org/content/38/6/1161.

6. WILDING JPH. The importance of weight management in type 2 diabetes mellitus. Int J Clin Pract. 2014 Jun; 68(6): 682–691. Available on: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4238418/

7. KODESH I. Associations between Variables Regarding Diabetes for Pima Indian Women. Available on: https://rpubs.com/ikodesh/53189.

8. DIABETES UK. Oral Glucose Tolerance Test. 2019. Available on: https://www.diabetes.co.uk/oral-glucose-tolerance-test.html

9. NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE - NICE. Guideline Hypertension in adults: diagnosis and management. 2019. Available on: https://www.nice.org.uk/guidance/ng136/documents/draft-guideline

10. BOER IH, BANGALORE S, BENETOS A, DAVIS AM, MICHOS ED, MUNTNER P, ROSSING P, ZOUNGAS S, BAKRIS G. Diabetes and Hypertension: A Position Statement by the American Diabetes Association. Diabetes Care 2017 Sep; 40(9): 1273-1284.

11. BLOOD PRESSURE UK. Low Blood Pressure. Available on: http://www.bloodpressureuk.org/microsites/u40/Home/facts/Whatislow#:~:text=For%20instance%2C%20when%20the%20heart,between%2040%20to%20160%20mmHg.

12. CHANDRA-SELVI E, PAVITHRA N, SAIKUMAR P. Skin Fold Thickness in Diabetes Mellitus: A Simple Anthropometric Measurement May Bare the Different Aspects of Adipose Tissue. IOSR Journal of Dental and Medical Sciences (IOSR-JDMS) e-ISSN: 2279-0853, p-ISSN: 2279-0861.Volume 15, Issue 11 Ver. IX (November. 2016), PP 07-11.

13. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287763/

14. https://www.sciencedirect.com/science/article/pii/S1646343913000734

15. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3417105/