Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

Konstantonis, George; Singh, Krishna V.; Sfikakis, Petros P.; Jamthikar, Ankush; Kitas, George D.; Gupta, Suneet Kr.; Saba, Luca; Verrou, Kleio; Khanna, Narendra N.; Ruzsa, Zoltán; Sharma, Aditya; Laird, John R.; Johri, Amer M.; Kalra, Mannudeep K.; Protogerou, Athanase D.; Suri, Jasjit S.

doi:10.1007/s00296-021-05062-4

Cited by 22 publications

(9 citation statements)

References 70 publications

(72 reference statements)

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“…However, the sample distribution of each class is imbalanced, and the imbalanced data set in terms of end points leads to a bias toward the majority class when predicting the boundary separation, which affects the performance of the ML classifier. 36 Therefore, the synthetic minority oversampling technique (SMOTE) was used to avoid the problem of imbalance class. 37,38 The SMOTE is based on the nearest-neighbor technique to generate nonoverlapping samples for the minority class.…”

Section: Correlation Analysis and Fpmmentioning

confidence: 99%

Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Zhang,

He,

Wang

et al. 2023

ACS EST Water

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Multidimensional indicators of surface water are key to assessing the water quality. Cost and time could be saved if surface water can be accurately assessed by fewer indicators. Therefore, it is necessary to screen key water quality indicators for different basins. This study collected 35 water quality indicators (42 315 observations) along the Wujingang basin. Cluster analysis and correlation coefficients were used to identify homogeneous categories of water quality indicators. Frequent pattern mining (FPM) was used to remove redundant indicators. Finally, the water quality assessment after the removal of redundant indicators was validated by classification analysis. Results of the silhouette coefficient and within-cluster sum of squared errors indicated that K-means was the optimal clustering model. Concomitant indicators Pb, Cl − , NO 3− , TN, V, and Al were identified using FPM. Decision tree verified that the performance did not decrease after removing Pb, Cl − , V, NO 3 − −N, and Al. These indicators were redundant for the Wujingang basin and could be monitored less frequently when there is no special use. This study provides important information for developing a selection framework based on multidimensional water quality data, which could serve as a baseline system for the selection of key water quality indicators in a specific basin.

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Section: Correlation Analysis and Fpmmentioning

confidence: 99%

Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Zhang,

He,

Wang

et al. 2023

ACS EST Water

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“…We specifically employed the Min-Max Scaler method, which rescales the data to a fixed range between 0 and 1. This is achieved by subtracting the minimum value and dividing by the range [74,75]. By utilizing this method, we standardized the features and reduced their values, which expedited the training process for both ML and DL models.…”

Section: Quality Controlmentioning

confidence: 99%

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

Singh

Khanna

Rout

et al. 2023

Preprint

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Background and Motivation: Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present GeneAI 3.0 (AtheroPoint™, Roseville, CA, USA), a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. Method: GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a compositefeature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of five EML and six EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11+27=38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. Results: The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL>HDL>SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Conclusions: Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

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“…However, another investigation found a statistically comparable AUC for predicting stroke using a complex logistic regression model fed with laboratory data compared to the Framingham Risk Model [ 147 ]. Remarkably, in a recent investigation, ML classifiers outperformed the classical cardiovascular disease risk score when they were fed with cardiovascular risk factors, including conventional risk factors, laboratory-based blood biomarkers, and ultrasound images [ 148 ].…”

Section: Artificial Intelligence In Ramentioning

confidence: 99%

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

2022

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Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of Sara Momtazmanesh and Ali Nowroozi have contributed equally to this work and share first authorship.

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Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

Cited by 22 publications

References 70 publications

Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

Machine-Learning-Based Approach To Assessing Water Quality in a Specific Basin: The Case of Wujingang Basin

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

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