💡[Feature]: in the existing repo of Fetal Health Classification,I want to add XGBoost and LightGBM models which helps in increases the accuracy of the prediction

[shravya312]

Is there an existing issue for this?

• I have searched the existing issues

Feature Description

XGBoost Strengths:

Optimized for speed and performance.
Works great with structured/tabular data.
Handles missing data efficiently.
Good for dealing with imbalanced datasets.

Best for:

Complex datasets where features interact in non-obvious ways.
Cases where accuracy is more important than making the model easy to explain.

LightGBM Strengths:

Very fast training, even on large datasets.
Works well with many features and can handle missing or sparse data.
Best for:

Large datasets with many rows and columns.
Often faster than XGBoost while still giving good accuracy.
Accuracy potential:

Similar or slightly better than XGBoost, especially for large or high-dimensional data.
Best use-case:

If you need faster training while maintaining accuracy similar to XGBoost.

As of now in the project these have used with 96% highest accuracy

Logistic Regression
Decision Tree Classifier
Random Forest Classifier
Gradient Boosting Classifier

Use Case

Use Case:
Scenario: Expecting mothers go for regular check-ups during pregnancy, where doctors monitor the health of the fetus using tests like Cardiotocogram (CTG).

Problem: Sometimes, it’s hard to tell if a fetus is healthy or if there might be problems. Mistakes can happen, leading to undetected issues that could harm the baby or mother.

Solution: Use machine learning models to analyze the CTG data and classify the fetal health into three categories: normal, suspect, and pathological.

Benefits

Early Detection of Risks: Better accuracy helps find problems early, allowing timely medical help for mothers and babies.

Reducing Infant Mortality: Accurate predictions lower the chances of stillbirths and deaths by identifying high-risk pregnancies needing extra care.

Better Resource Allocation: Hospitals can use resources wisely, focusing on high-risk cases for timely support.

Improved Maternal Health: Accurate predictions lower the risk of undiagnosed issues, helping to keep mothers healthy during childbirth.

Increased Trust in Technology: Higher accuracy builds trust in AI tools, helping doctors and families rely on technology for better care.

Reducing Healthcare Costs: Finding problems early cuts down on expensive emergencies and long-term care needs.

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Priority

High

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[github-actions]

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[shravya312]

I want to improve the code of fetal health classification by adding 2 models XGboost and LightGBM which increases the accuracy
Can u assign this issue to me @sanjay-kv

[github-actions]

Hello @shravya312! Your issue #1233 has been closed. Thank you for your contribution!