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# Low Birth Weight Prediction with Machine Learning Algorithms
Based on the dataset, this algorithm predicts the possibility of low birth weight of a child given the following set of features:
* Age of the Mother in Years
* Weight in Pounds at the Last Menstrual Period
* Race (1 = White, 2 = Black, 3 = Other)
* Smoking Status During Pregnancy (1 = Yes, 0 = No)
* History of Premature Labor (0 = None, 1 = Yes)
* History of Hypertension (1 = Yes, 0 = No)
* Presence of Uterine Irritability (1 = Yes, 0 = No)
Using this feature set (x0, x1 .. x6), higher order features were generated to get greater accuracy of prediction. I used Logistic Regression along with python modules scipy and numpy for this purpose.
After training on the dataset, I predicted the Low Birth Weight (0 = No, 1 = Yes) values for the same data-set and compared it against the actual values. With this the accuracy of prediction was calculated.
With feature mapping and regularization the algorithm achieved an accuracy of 82.14%.
# Dataset used for Logistic Regression
## Source of data
Hosmer and Lemeshow (2000) Applied Logistic Regression: Second
Edition. These data are copyrighted by John Wiley & Sons Inc. and must
be acknowledged and used accordingly. Data were collected at Baystate
Medical Center, Springfield, Massachusetts.
## Description of data
Look at the *data_description* file for more details.
# Credits
* Hosmer and Lemeshow for providing the dataset.
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