Feature importance in machine learning models: A fuzzy information fusion approach

Rengasamy, Divish; Mase, Jimiama Mafeni; Kumar, Aayush; Rothwell, Benjamin; Torres, Mercedes Torres; Alexander, Morgan R.; Winkler, David A.; Figueredo, Grazziela P.

doi:10.1016/j.neucom.2022.09.053

“…Feature importance was generated by randomly permutating one chromosome at a time in the testing data and evaluating its impact on the performance of each ML model. 45 Interestingly, non-linear classification models RF, XGB, CGB, and ExSTraCS all deemed chr2 as most important. In particular, RF and CGB based their models almost exclusively on the GAPF HCB data from chr2, which stand in contrast to our threshold model.…”

Section: Resultsmentioning

confidence: 99%

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA

Murata,

Igari,

Urbanowicz

et al. 2023

Preprint

0

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Interrogating plasma cell-free DNA (cfDNA) to detect cancer offers promise; however, no current tests scan structural variants (SVs) throughout the genome. Here, we report a simple molecular workflow to enrich a tumorigenic SV (DNA palindromes/fold-back inversions) that often demarcates genomic amplification and its feasibility for cancer detection by combining low-throughput next-generation sequencing with automated machine learning (Genome-wide Analysis of Palindrome Formation, GAPF-seq). Tumor DNA signal manifested as skewed chromosomal distributions of high-coverage 1-kb bins (HCBs), differentiating 39 matched breast tumor DNA from normal DNA with an average AUC of 0.9819. In a proof-of-concept liquid biopsy study, cfDNA from 0.5 mL plasma from prostate cancer patients was sufficient for binary classification against matched buffy coat DNA with an average AUC of 0.965. HCBs on the X chromosome emerged as a determinant feature and were associated with AR amplification. GAPF-seq could generate unique cancer-specific SV profiles in an agnostic liquid biopsy setting.

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“…Let us compute and identify the most influential risk factor for cancer in Ethiopia before developing a cancer severity detection model. The significance of features for cancer severity level is calculated using split points that improve the performance of the cancer severity detection measure and is weighted by the number of observations handled by the node [ 37 ]. The feature importance is built using the decision tree’s nodes and the features used to build the tree.…”

Section: Resultsmentioning

confidence: 99%

Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning

Endalie

¹

,

Abebe

²

2023

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Cancer is a broad term that refers to a wide range of diseases that can affect any part of the human body. To minimize the number of cancer deaths and to prepare an appropriate health policy on cancer spread mitigation, scientifically supported knowledge of cancer causes is critical. As a result, in this study, we analyzed lung cancer risk factors that lead to a highly severe cancer case using a decision tree-based ranking algorithm. This feature relevance ranking algorithm computes the weight of each feature of the dataset by using split points to improve detection accuracy, and each risk factor is weighted based on the number of observations that occur for it on the decision tree. Coughing of blood, air pollution, and obesity are the most severe lung cancer risk factors out of nine, with a weight of 39%, 21%, and 14%, respectively. We also proposed a machine learning model that uses Extreme Gradient Boosting (XGBoost) to detect lung cancer severity levels in lung cancer patients. We used a dataset of 1000 lung cancer patients and 465 individuals free from lung cancer from Tikur Ambesa (Black Lion) Hospital in Addis Ababa, Ethiopia, to assess the performance of the proposed model. The proposed cancer severity level detection model achieved 98.9%, 99%, and 98.9% accuracy, precision, and recall, respectively, for the testing dataset. The findings can assist governments and non-governmental organizations in making lung cancer-related policy decisions.

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“…Feature importance was generated by randomly permutating one chromosome at a time in the testing data and evaluating its impact on the performance of each ML model. 45 Interestingly, non-linear classi cation models RF, XGB, CGB, and ExSTraCS all deemed chr2 as most important. In particular, RF and CGB based their models almost exclusively on the GAPF HCB data from chr2, which stand in contrast to our threshold model.…”

Section: Machine Learning Algorithms For Binary Classi Cationmentioning

confidence: 99%

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA

Tanaka,

Murata,

Igari

et al. 2024

Preprint

0

View full text Add to dashboard Cite

Interrogating plasma cell-free DNA (cfDNA) to detect cancer offers promise; however, no current tests scan structural variants (SVs) throughout the genome. Here, we report a simple molecular workflow to enrich a tumorigenic SV (DNA palindromes/fold-back inversions) that often demarcates genomic amplification and its feasibility for cancer detection by combining low-throughput next-generation sequencing with automated machine learning (Genome-wide Analysis of Palindrome Formation, GAPF-seq). Tumor DNA signal manifested as skewed chromosomal distributions of high-coverage 1-kb bins (HCBs), differentiating 39 matched breast tumor DNA from normal DNA with an average AUC of 0.9819. In a proof-of-concept liquid biopsy study, cfDNA from 0.5 mL plasma from prostate cancer patients was sufficient for binary classification against matched buffy coat DNA with an average AUC of 0.965. HCBs on the X chromosome emerged as a determinant feature and were associated with AR amplification. GAPF-seq could generate unique cancer-specific SV profiles in an agnostic liquid biopsy setting.

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Feature importance in machine learning models: A fuzzy information fusion approach

Cited by 28 publications

References 27 publications

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA

Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA

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