# Predicting Blood Glucose Levels in Patients

## I. Predicting Blood Glucose Levels in Patients

Effective management of blood glucose levels requires continuous monitoring of patients’ records, which can be obtained by combining data from electronic and paper records. This comprehensive approach enables healthcare professionals to gain a better understanding of how various factors, such as diet, medication, and physical activity, affect blood glucose levels. With this information, healthcare professionals can make informed decisions about treatment options, adjust medication dosages, and provide personalized advice to patients. This approach enhances the accuracy and timeliness of information available to healthcare professionals, improving the overall management of the condition.

Machine learning techniques can help healthcare professionals manage blood glucose levels more efficiently by automating the analysis of patient records. This can lead to better patient outcomes, lower healthcare costs, and improved quality of life. By using algorithms to analyze patient data, medical professionals can gain insights into patients’ health status, making it easier to make informed decisions about treatment options. This technology has the potential to revolutionize the healthcare industry by enabling faster and more accurate decision-making, ultimately improving the well-being of patients and the efficiency of healthcare systems.

## II. Problem Statement

The goal of this competition is to predict the blood glucose levels of patients after breakfast, lunch, and dinner. For that, we need to extract meaningful information from the available data and use a combination of domain expertise and machine learning techniques to build robust and accurate predictive models. By creating novel features that capture important aspects of the data, participants can improve the performance of their prediction models. These new features may reveal previously unknown relationships or patterns that can help better understand the underlying factors that contribute to blood glucose levels. Ultimately, the aim is to create a prediction model that accurately identifies patients with high or low blood glucose levels and helps healthcare professionals to provide targeted and effective treatments. 

The goal of this competition is to extract meaningful information from the available data and use a combination of domain expertise and machine learning techniques to build robust and accurate predictive models. By creating novel features that capture important aspects of the data, participants can improve the performance of their prediction models. These new features may reveal previously unknown relationships or patterns that can help better understand the underlying factors that contribute to blood glucose levels. Ultimately, the aim
is to create a prediction model that accurately predicts the blood glucose levels of patients after breakfast, lunch, and dinner and helps healthcare professionals to provide targeted and effective treatments.

We challenge participants to generate new features from a dataset containing patient records obtained from two sources: an automatic electronic recording device and paper records. The primary objective of this competition is to improve predictive models for diabetes-related outcomes by creating new features that capture meaningful information from the available data.

To succeed in this competition, participants will need to demonstrate a deep understanding of the data and the underlying biology of diabetes. They will need to use creative and innovative approaches to feature engineering, such as time-series analysis, statistical modeling, and machine-learning techniques.

## Task 1: Feature Engineering

**You have to make a dataset named “Engineered-single-Features.csv” which should contain the following features for each timestamp in a single file.**
1. patient_id (patient unique identifier)
2. time_of_day (e.g., morning, afternoon, evening, night)
3. day_of_week (e.g., Monday, Tuesday, Wednesday, Thursday
4. year
5. month
6. time_since_beginning (in seconds)
7. week_day (either true or false)
8. quarter (1 of 4 quarters in a day)
9. glucose_level_since_beginning (initially 0)
10. typical_exercise_activity
11. more_than_usual_exercise_activity
12. less_than_usual_exercise_activity
13. unspecified_special_event
14. diabetic_status_since_beginning (low, moderate, high)
15. pre_breakfast_blood_glucose_measurement
16. pre_lunch_blood_glucose_measurement
17. pre_supper_blood_glucose_measurement
18. Pre_snack_blood_glucose_measurement
19. post_breakfast_blood_glucose_measurement
20. post_lunch_blood_glucose_measurement
21. post_supper_blood_glucose_measurement
22. regular_insulin_dose
23. NPH_insulin_dose
24. UltraLente_insulin_dose
25. hypoglycemic_symptoms
26. typical_meal_ingestion
27. more_than_usual_meal_ingestion
28. less_than_usual_meal_ingestion

**Now, create a dataset named “Engineered-aggregated-Features.csv” which should contain the following features for each patient.**

1. average_change_breakfast (Average change in blood glucose level before and after breakfast)
2. average_change_lunch (Average change in blood glucose level before and after lunch)
3. average_change_supper (Average change in blood glucose level before and after supper)
4. average_glucose (Average glucose level throughout)
5. maximum_glucose_level (Maximum glucose level)
6. minimum_glucose_level (Minimum glucose level)
7. average_time_between_meals (Average time between meals )

## Task 2: Data Analysis and Visualization

Perform the exploratory data analysis on the dataset created above and answer the following questions. Please note that your claims must be supported by visualizations.

1. Determine the patient exhibiting the highest degree of glucose level stability in comparison to the rest of the patient population. This involves analyzing the standard deviation of glucose levels across all patients and identifying the individual with the lowest value. This approach provides insight into which patient has the most consistent glucose levels over time, thereby aiding healthcare professionals in designing tailored treatment plans to maintain optimal glucose levels.
2. Glucose levels in the body vary throughout the day. The levels typically rise after meals and then gradually decline over time. This fluctuation in glucose levels is affected by factors such as food intake, physical activity, and insulin production. To what extent does the data support or contradict the statement? Can you describe the pattern of change in glucose levels throughout the day? Specifically, how do glucose levels vary over time, from waking up in the morning to going to bed at night?
3. What is the correlation between diet and exercise with blood glucose levels over time, and how does it compare to the correlation between food intake or physical activity alone and blood glucose levels?”. This question requires visualization of data to analyze the relationship between diet, exercise, food intake, physical activity, and blood glucose levels. By comparing the correlations between these variables, it may be possible to
determine whether the statement that diet and exercise both affect blood glucose levels is supported by the data.

## Task 3: Prediction Labels

** Make predictions of the following attributes**

1. pre_breakfast_blood_glucose_measurement
2. pre_lunch_blood_glucose_measurement
3. pre_supper_blood_glucose_measurement

Overall, the key to success in this competition will be to think creatively about how to extract meaningful information from the available data and to use a combination of domain expertise and machine learning techniques to build robust and accurate predictive models.

Good luck!

## Submission Instructions

When submitting your predictions for a Kaggle competition, there are a few key instructions to follow. Your submission should consist of a single file that includes both a unique ID and its corresponding prediction value. 

Furthermore, it is crucial that the IDs in your submission file match exactly with the IDs in the test dataset. Any discrepancies in the IDs can lead to errors and inaccuracies in the judging process, which could negatively impact your overall score.

By following these instructions, you can successfully submit your predictions and participate in the Kaggle competition. It is essential to pay close attention to the details and ensure that your submission is accurate and free from errors. With a bit of careful preparation and attention to detail, you can increase your chances of success and potentially win the competition.