A novel deep learning model based on convolutional neural networks for employee churn prediction

Problem

Companies suffer performance losses when valuable employees quit, so they need systems that predict employee churn in advance.

Why it matters

Losing valuable employees significantly affects company performance, making early prediction critical for retention.

What the AI did

Applied a CNN to numerical retail employee data, then proposed a hybrid extended convolutional decision tree (ECDT) and a grid-search-optimized variant (ECDT-GRID) to improve classification accuracy.

Results

ECDT-GRID outperformed the CNN and ECDT models; abstract truncated before further benchmark details.

Impact

Evidence only supports a churn-prediction model outperforming its own baselines on one retail dataset.

Record

Importance rating

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