# Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data

arXiv: https://arxiv.org/abs/2402.14980 · HTML: https://mlsanity.com/paper/2402.14980/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 43 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features](https://mlsanity.com/paper/2104.00629/) (2021)
- [Model Optimization in Imbalanced Regression](https://mlsanity.com/paper/2206.09991/) (2022)
- [Predicting loss-of-function impact of genetic mutations: a machine learning approach](https://mlsanity.com/paper/2402.00054/) (2024)

## Built on by (most cited first, 1 of 1)

- ["FRAME: Forward Recursive Adaptive Model Extraction-A Technique for Advance Feature Selection"](https://mlsanity.com/paper/2501.11972/) (2025)

## Abstract (arXiv)

Rapid advancements in genome sequencing have led to the collection of vast amounts of genomics data. Researchers may be interested in using machine learning models on such data to predict the pathogenicity or clinical significance of a genetic mutation. However, many genetic datasets contain imbalanced target variables that pose challenges to machine learning models: observations are skewed/imbalanced in regression tasks or class-imbalanced in classification tasks. Genetic datasets are also often high-cardinal and contain skewed predictor variables, which poses further challenges. We aimed to investigate the effects of data preprocessing, feature selection techniques, and model selection on the performance of models trained on these datasets. We measured performance with 5-fold cross-validation and compared averaged r-squared and accuracy metrics across different combinations of techniques. We found that outliers/skew in predictor or target variables did not pose a challenge to regression models. We also found that class-imbalanced target variables and skewed predictors had little to no impact on classification performance. Random forest was the best model to use for imbalanced regression tasks. While our study uses a genetic dataset as an example of a real-world application, our findings can be generalized to any similar datasets.