Improved feature selection and classification for rheumatoid arthritis disease using weighted decision tree approach (REACT)

Shanmugam, S.; Preethi, J.

doi:10.1007/s11227-019-02800-1

Cited by 13 publications

(4 citation statements)

References 19 publications

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“…39 Models constructed toward RA prediction considered a wide range of potential risk factors under consideration, including genetic, socio-demographic, and environmental factors, comorbidities, and results of clinical and laboratory examination. [41][42][43][44][45] However, interactions between these factors were not included in most of these models -except when a specific interaction is already known or suspected 41,44 -as that would otherwise exponentially increase the computational cost. 45 In this context, our observation of synergistic interactions of depression with obesity and HTG could provide important insights to incorporate these specific interactions in a prediction model to improve prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

A stronger association of depression with rheumatoid arthritis in presence of obesity and hypertriglyceridemia

Shand

Fuller

Lufkin

et al. 2023

Preprint

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Background. Rheumatoid arthritis (RA) is an autoimmune disorder characterized by chronic and systemic inflammation. Recent research underscores the role of chronic inflammation in multiple common RA comorbidities such as depression, obesity, and cardiovascular diseases (CVDs), suggesting a potential overlap of the pathogenic mechanisms for RA. However, it is not well understood how the coexistence of these comorbid conditions impacts the risk of RA and whether any such association relates to the inflammatory status of the body. Methods. We used data from the 2007-2010 United States National Health and Nutrition Examination Survey (NHANES) database and compared RA prevalence between subsamples with the presence of any two conditions among depression, obesity, and hypertriglyceridemia (HTG). Each subsample was further divided into three categories based on the serum level of the inflammatory marker C-reactive protein (CRP) and analyzed for statistically significant differences using three-way χ2 tests of independence. Results. The study was conducted on 4,136 patients who fulfilled the inclusion criteria (representing 163,540,241 individuals after adjustment for sampling weights). Rates of depression, obesity, and HTG were found to be significantly higher (P < 0.001) among the subjects with RA compared with the control population with no arthritis. The presence of depression along with obesity or HTG showed a noticeably higher RA prevalence but such an association was not observed for the combination of obesity and HTG. The synergistic effect of HTG with depression was found to be most prominent at a medium CRP level (1-3 mg/L), while for obesity, the effect was observed across all CRP levels examined. These findings were further confirmed by the three-way χ2 test for independence. Conclusions. The presence of obesity or HTG in subjects suffering from depression might pose an increased risk of RA. Inflammatory mechanisms potentially play an important underlying role as suggested by the strong dependency of the association to CRP level. Identification of synergistic associations between RA risk conditions could provide useful information to predict the development and progress of RA.

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Section: Discussionmentioning

confidence: 99%

A stronger association of depression with rheumatoid arthritis in presence of obesity and hypertriglyceridemia

Shand

Fuller

Lufkin

et al. 2023

Preprint

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“…Machine learning methods for early prediction of RA based on electronic health records [25][26][27][28][29], deep learning strategy on X-ray images [30], an ensemble approach for disease gene identification, where EPU achieved an accuracy of 84.8% [31]. The Decision Stump as weak Learner, and Cuckoo search named CS-Boost for early prognosis of the disease [32].…”

Section: Related Workmentioning

confidence: 99%

Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

Shanmugam¹,

Saravanabhavan²,

Arunkumar³

2021

Preprint

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One of the severe auto immune diseases that affects the entire human body is Rheumatoid Arthritis (RA), the disease triggers one’s immune system to attack the inner linings of bones and causes severe inflammation of the synovium. The continuous erosion of bone lining leads to permanent loss of the joint, accounting this severity the early prognosis of the disease is a significant and inevitable process. But, the sign and symptoms of the disease are always uncertain. The symptom of RA disease is similar to other inflammatory diseases, so highly experienced experts can identify the disease in its early stage. To support the clinicians and technicians for early prognosis of the disease, a computer-aided decision support model based on Harmony Search –Adaptive Neuro Fuzzy Inference System is presented in this study. The Harmony search algorithm is employed to select the optimal features, and ANFIS is adopted to perform classification. To demonstrate the effectiveness of the model, metrics such as Accuracy, Sensitivity, Specificity, Precision, Recall, F-measure, Positive Predictive Value, Negative Predictive Value, Root Mean Square Error, and Mean Absolute Error are employed and evaluated in MATLAB simulation environment. The proposed HS-ANFIS outperformed other models developed in this research and existing works of literature.

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“…Studies have aimed to predict the occurrence of common diseases like CVD to provide early diagnosis or risk assessment using data mining, machine learning algorithms, and mathematical modeling (32). While some studies have attempted to predict RA using a similar approach (33,34), these studies were neither very selective in defining relevant factors for disease prediction nor did consider their interactions. Karlson et.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions

Lufkin

Budišić

Mondal

et al. 2021

Front. Public Health

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Rheumatoid arthritis (RA) is a chronic autoimmune disorder that commonly manifests as destructive joint inflammation but also affects multiple other organ systems. The pathogenesis of RA is complex where a variety of factors including comorbidities, demographic, and socioeconomic variables are known to associate with RA and influence the progress of the disease. In this work, we used a Bayesian logistic regression model to quantitatively assess how these factors influence the risk of RA, individually and through their interactions. Using cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), a set of 11 well-known RA risk factors such as age, gender, ethnicity, body mass index (BMI), and depression were selected to predict RA. We considered up to third-order interactions between the risk factors and implemented factor analysis of mixed data (FAMD) to account for both the continuous and categorical natures of these variables. The model was further optimized over the area under the receiver operating characteristic curve (AUC) using a genetic algorithm (GA) with the optimal predictive model having a smoothed AUC of 0.826 (95% CI: 0.801–0.850) on a validation dataset and 0.805 (95% CI: 0.781–0.829) on a holdout test dataset. Apart from corroborating the influence of individual risk factors on RA, our model identified a strong association of RA with multiple second- and third-order interactions, many of which involve age or BMI as one of the factors. This observation suggests a potential role of risk-factor interactions in RA disease mechanism. Furthermore, our findings on the contribution of RA risk factors and their interactions to disease prediction could be useful in developing strategies for early diagnosis of RA.

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Improved feature selection and classification for rheumatoid arthritis disease using weighted decision tree approach (REACT)

Cited by 13 publications

References 19 publications

A stronger association of depression with rheumatoid arthritis in presence of obesity and hypertriglyceridemia

A stronger association of depression with rheumatoid arthritis in presence of obesity and hypertriglyceridemia

Effective Prediction of Rheumatoid Arthritis (Ra) Diagnosis Using Hybrid Harmony Search with Adaptive Neuro Fuzzy Inference System

A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions

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