Similarities and Differences between Machine Learning and Traditional Advanced Statistical Modeling in Healthcare Analytics

Bennett, Michele; Hayes, Karin; Kleczyk, Ewa J.; Mehta, Rajesh

doi:10.48550/arxiv.2201.02469

Cited by 3 publications

(3 citation statements)

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“…To complement the current ML approach, a rigorous statistical analysis was undertaken to assess the inferential strength of each individual variable. Statistical analysis tests hypotheses and infers causal relationships between individual variables and diabetes risk, while ML builds models and makes predictions based on the data, without necessarily explaining how the data is related or what causes the outcome [25]. However, statistical methods do not capture the nonlinear and interactive effects of multiple variables on diabetes risk.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the complementary strengths of both statistics and ML were leveraged to provide a more comprehensive understanding of the predictive factors for T2DM, allowing for prioritization of variables that may have been overlooked by traditional statistical analysis. As Bennett et al suggest, ML and statistical analysis are different but complementary methods that can provide different insights into the data, depending on the research question and the data available [25].…”

Section: Discussionmentioning

confidence: 99%

“…The analysis focused on white individuals. Participants of other races (158), Hispanic (37), those with other types of diabetes (133), individuals without genotype ID (25), and first-degree relatives (8) of the participants were excluded. The characteristics of the remaining 5,672 individuals, which includes both controls and T2DM individuals, are presented in Table 1.…”

Section: Data Preprocessingmentioning

confidence: 99%

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PyCaret for Predicting Type 2 Diabetes: A Phenotype and Gender‐Based Approach with the Nurses’ Health Study and the Health Professionals’ Follow‐Up Study

Gul,

Ayturan,

Hardalaç

2024

Preprint

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Predicting type 2 diabetes mellitus (T2DM) using phenotypic data with machine learning (ML) techniques has received significant attention in recent years. PyCaret, an ML tool that enables the simultaneous application of 16 different algorithms, was used to predict T2DM using phenotypic variables from the “Nurses’ Health Study” and “Health Professionals’ Follow-up Study” datasets. Ridge classifier, Linear Discriminant Analysis, and Logistic Regression (LR) were the best-performing models for the male-only data subset. For the female-only data subset, LR, Gra-dient Boosting Classifier, and CatBoost Classifier were the strongest models. The AUC, accuracy, and precision were 0.77, 0.70, and 0.70 for males and 0.79, 0.70, and 0.71 for females, respective-ly. The feature importance plot showed that familial history of diabetes (famdb), never having smoked, and high blood pressure (hbp) were the most influential features in females, while famdb, hbp, and current smoking were the major variables in males. In conclusion, PyCaret was used successfully for speed analysis for the prediction of T2DM by simplifying complex ML tasks. Gen-der differences are an important consideration for T2DM prediction. Despite this comprehensive ML tool, phenotypic variables alone may not be sufficient for early T2DM prediction; genotypic variables could also be used in combination for future studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

PyCaret for Predicting Type 2 Diabetes: A Phenotype and Gender‐Based Approach with the Nurses’ Health Study and the Health Professionals’ Follow‐Up Study

Gul,

Ayturan,

Hardalaç

2024

Preprint

View full text Add to dashboard Cite

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Application of artificial intelligence in trauma orthopedics: Limitation and prospects

Salimi,

Parry,

Shahrokhi

et al. 2023

World J Clin Cases

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The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previous studies. AI may be most promising in the diagnosis of less-obvious fractures that are more commonly missed. Future studies are necessary to develop more accurate and effective detection models that can be used clinically.

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Exploration of Digital Divide Factors Affecting Life Satisfaction in Older People: Using Tree-Based Ensemble Machine Learning and SHAP

Lee

2024

dcs

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Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.

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Similarities and Differences between Machine Learning and Traditional Advanced Statistical Modeling in Healthcare Analytics

Cited by 3 publications

References 0 publications

PyCaret for Predicting Type 2 Diabetes: A Phenotype and Gender‐Based Approach with the Nurses’ Health Study and the Health Professionals’ Follow‐Up Study

PyCaret for Predicting Type 2 Diabetes: A Phenotype and Gender‐Based Approach with the Nurses’ Health Study and the Health Professionals’ Follow‐Up Study

Application of artificial intelligence in trauma orthopedics: Limitation and prospects

Exploration of Digital Divide Factors Affecting Life Satisfaction in Older People: Using Tree-Based Ensemble Machine Learning and SHAP

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