problem img1

README.md

@@ -5,7 +5,7 @@ This code is part of the work done by me for the [Statistical and Machine Learni
 The reliable prediction of glucose levels in diabetes patients remains a pivotal challenge in biomedical engineering and healthcare informatics. Traditional predictive models have often suffered from inaccuracies and an inability to generalize across different physiological characteristics inherent in patient-specific data. The implications of these shortcomings are nontrivial and bear significant ramifications for clinical decision-making.
 In the following, we start from a reference paper and we try to reproduce and expand the work already done, to achieve accurate and robust time series forecasting for blood sugar levels prediction.
 
-The problem is illustrated in the Figure below. We have a series of Blood Glucose Reading at 5 minutes timesteps. We take as input the values of the previous half-hour ("Sampling Horizon") and perform a regression on the following half-hour values ("Prediciton Horizon"). We can also treat the problem as a classification one by looking at where our predictions fall: either above or below the *Hypoglicemia* threshold (80 mg/dL).
+The problem is illustrated in the Figure below. We have a series of Blood Glucose Readings at 5 minutes timestamps. We take as input the values of the previous half-hour ("Sampling Horizon") and perform a regression on the following half-hour values ("Prediciton Horizon"). We can also treat the problem as a classification by looking at where our predictions fall: either above or below the *Hypoglicemia* threshold (80 mg/dL).
 
 ![The problem](docs/img/problem.png)
 

docs/index.md

@@ -7,7 +7,7 @@ The following pages describe the work done by me[^1] as part of the [Statistical
 The reliable prediction of glucose levels in diabetes patients remains a pivotal challenge in biomedical engineering and healthcare informatics. Traditional predictive models have often suffered from inaccuracies and an inability to generalize across different physiological characteristics inherent in patient-specific data. The implications of these shortcomings are nontrivial and bear significant ramifications for clinical decision-making.
 In the following, we start from a reference paper and we try to reproduce and expand the work already done, to achieve accurate and robust time series forecasting for blood sugar levels prediction.
 
-The problem is illustrated in the Figure below. We have a series of Blood Glucose Reading at 5 minutes timesteps. We take as input the values of the previous half-hour ("Sampling Horizon") and perform a regression on the following half-hour values ("Prediciton Horizon"). We can also treat the problem as a classification one by looking at where our predictions fall: either above or below the *Hypoglicemia* threshold (80 mg/dL).
+The problem is illustrated in the Figure below. We have a series of Blood Glucose Readings at 5 minutes timestamps. We take as input the values of the previous half-hour ("Sampling Horizon") and perform a regression on the following half-hour values ("Prediciton Horizon"). We can also treat the problem as a classification by looking at where our predictions fall: either above or below the *Hypoglicemia* threshold (80 mg/dL).
 
 ![The problem](img/problem.png)