To predict the Diabetes Risk of an individual from the data collected via simple telephonic survey questions which will result in early diagnosis that may lead to more effective treatments & consulting. Medical Insurance and Fitness companies can also make targeted sales by this predictive machine learning models.
- Sabarivasan R
- Cibi Chellasamy A
- Varshiney M
- Arun VG
The Goal is to predict the diabetes risk of a person from the data collected via simple survey questions will result in early diagnosis which can lead to lifestyle changes and more effective treatment, making predictive models for diabetes risk an important tools for public and public health officials. Medical Insurance and Fitness company can make a targeted sale on the population.
The CDC estimates that 1 in 5 diabetics, and roughly 8 in 10 pre diabetics are unaware of their risk. While there are different types of diabetes, type II diabetes is the most common form and its prevalence varies by age, education, income, Blood Pressure, Cholesterol and other social determinants of health.
Data gathered from Kaggle.com. Appropriate Data cleaning done for the EDA. After EDA, Statistical tests are done to understand the behavior of variables. To tackle im-balancing used over sampling techniques. Different machine learning models built to predict the Risk Factors of Diabetes
Python, Visualization, Statistical Tests, Logistic Classification, Bagging & Boosting techniques, etc.