Predicting Pediatric Appendicitis using Ensemble Learning Techniques

Pati, Abhilash; Panigrahi, Amrutanshu; Nayak, Debashish Swapnesh Kumar; Sahoo, Ghanashyam; Singh, Debabrata

doi:10.1016/j.procs.2023.01.095

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…In this study, the WBBN model undergoes thorough training and testing using a distinct distribution of the PIDD dataset comprising 768 records. The primary evaluative parameter employed in this study is accuracy, which is the correctness of predictions made by a predictive model for diabetes diagnosis or classification (Nayak et al, 2023;Panigrahi et al, 2023;Pati et al, 2023).…”

Section: Resultsmentioning

confidence: 99%

Weighted Bayesian Belief Network for diabetics: a predictive model

Kharya,

Soni,

Pati

et al. 2024

Front. Artif. Intell.

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Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the “diabetes capital of the world” owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

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Section: Resultsmentioning

confidence: 99%

Weighted Bayesian Belief Network for diabetics: a predictive model

Kharya,

Soni,

Pati

et al. 2024

Front. Artif. Intell.

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“…A system with Windows 11, an Intel Core i7 CPU with 3.8 GHz clock speed, 16 GB of RAM, and 500 GB SSD is used to evaluate the proposed ensemble models. Following a methodical experimental procedure, these performance measures hope to build a class confusion matrix closer to reality than expectations [24], [25]. Performance evaluations in this research may be done using several performance indicators, such as Accuracy (AC), precision (PR), balanced accuracy (BA), mathew's correlation coefficient (MCC), false positive rate (FPR), false negative rate (FNR), F1-score (FS), specificity (SP), and sensitivity (SN) [26], [27].…”

Section: Resultsmentioning

confidence: 99%

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Sahoo,

Nayak,

Tripathy

et al. 2024

IJEECS

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Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substantial influence on the quality of life of cancer patients is the fear that they may experience a relapse of their disease. The objective of the study is to give medical practitioners a more effective strategy for using ensemble learning techniques to forecast when breast cancer may recur. This research aimed to investigate the usage of deep neural networks (DNNs) and artificial neural networks (ANNs) in addition to machine learning (ML) based approaches, including bagging, averaging, and voting, to enhance the efficacy of breast cancer relapse diagnosis on two breast cancer relapse datasets. Results from the empirical study demonstrate that the proposed ensemble learning-enabled approach improves accuracies by 96.31% and 95.81%, precisions by 96.70% and 96.15%, sensitivities by 98.88% and 98.68%, specificities by 84.62% in both, F1-scores by 97.78% and 97.40%, and area under the curve (AUCs) of 0.987 and 0.978, with University Medical Centre, Institute of Oncology (UMCIO) and Wisconsin prognostic breast cancer (WPBC) datasets respectively. Consequently, these improved disease outcomes may encourage physicians to use this model to make better treatment choices.

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“…The proposed model is developed on a workstation with 32GB of RAM, 1 TB of SSD storage, an Intel Core i7 processor, and a Ubuntu 20.04 operating system. The predictive model's performance can be evaluated in several ways [25][26][27]. This study was conducted with the Integrated CVD dataset in mind, along with four other HDDs that can be downloaded for free from the UCI-ML repository and a handful of performance metrics like accuracy, precision, sensitivity, specificity, F1-Score, etc., as shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts

Debasish Swapnesh Kumar Nayak

2023

IJRITCC

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One-third of all deaths worldwide yearly are attributable to cardiovascular disease (CVD). In contrast to the 7% of the wealthy who experience premature death, 43% of the poor do. Lifestyle diseases like obesity and diabetes are to blame. The importance of early identification of heart disease was demonstrated, and premature mortality was kept to a minimum. Combining clinical and biochemical data is essential for the early diagnosis of heart illness. Numerous IoT-enabled wearable healthcare applications have been created and released in recent years. Although the ability of wearable devices to share patient health data is expanding, it remains challenging to predict and identify health problems. Security, data storage, and patient monitoring are all part of the system. Artificial intelligence (AI) therapies may one day change the face of cardiology by providing doctors with cutting-edge data analysis and therapeutic decision-making resources. As the volume and complexity of data continue to increase, AI tools like machine learning (ML) and deep learning (DL) can assist medical professionals in learning more. Suppose we want to provide medical care to the elderly and those with chronic illnesses in the comfort of their own homes. In that case, we must upgrade our communication and information technology systems. The implemented DNN model's accuracy is amazing at 95.34 % and can yield other noteworthy outcomes when used to identify CVDs. We discuss and suggest the most suitable AI-IoT models for early CVD prediction and detection to reduce computational costs and increase time efficiency.

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Predicting Pediatric Appendicitis using Ensemble Learning Techniques

Cited by 8 publications

References 8 publications

Weighted Bayesian Belief Network for diabetics: a predictive model

Weighted Bayesian Belief Network for diabetics: a predictive model

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Enhancing Cardiovascular Disease Prediction Based on AI and IoT Concepts

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