Classification of pathological disorders in children using random forest algorithm

Bebortta, Sujit; Panda, Manoranjan; Panda, Shradhanjali

doi:10.1109/ic-etite47903.2020.253

Cited by 25 publications

(3 citation statements)

References 21 publications

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“…We compare our method to the existing methodologies (viz., QRL AND DRL) using the performance metrics like precision, recall, f-measure, and prediction accuracy for determining the prevalence of heart disease. The precision, recall, f-measure and accuracy can be mathematically represented as [41][42][43][44] × 100 (23) where 𝑇 𝑝 , 𝑇 𝑛 , 𝐹 𝑝 , 𝐹 𝑛 denote the true positive, true negative, false positive, and false negative respectively. The notations 𝑃𝑟, 𝑅, 𝑓 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒, and 𝐴 denote the precision, recall, f-measure and accuracy of the model respectively.…”

Section: B Resultsmentioning

confidence: 99%

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Bebortta

Tripathy²,

Basheer

et al. 2023

IEEE Access

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The prevalence of heart disease has remained a major cause of mortalities across the world and has been challenging for healthcare providers to detect early symptoms of cardiac patients. To this end, several conventional machine learning models have gained popularity in providing precise prediction of heart diseases by taking into account the underlying conditions of patients. The drawbacks associated with these methods are a lack of generalization and the convergence rate of these methods being much slower. As the healthcare data associated with these systems scale up leading to healthcare big data issues, a Cloud-Fog computing-based paradigm is necessary to facilitate low-latency and energy-efficient computation of the healthcare data. In this paper, a DeepMist framework is suggested which exploits Deep Learning models operating over Mist Computing infrastructure to leverage fast predictive convergence, low-latency, and energy efficiency for smart healthcare systems. We exploit the Deep Q Network (DQN) algorithm for building the prediction model for identifying heart diseases over the Mist computing layer. Different performance evaluation metrics, like precision, recall, f-measure, accuracy, energy consumption, and delay, are used to assess the proposed DeepMist framework. It provided an overall prediction accuracy of 97.6714 % and loss value of 0.3841, along with energy consumption and delay of 32.1002 mJ and 2.8002 ms respectively. To validate the efficacy of DeepMist, we compare its outcomes over the heart disease dataset in convergence with other benchmark models like Q-Reinforcement Learning (QRL) and Deep Reinforcement Learning (DRL) algorithms and observe that the proposed scheme outperforms all others.

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Section: B Resultsmentioning

confidence: 99%

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Bebortta

Tripathy²,

Basheer

et al. 2023

IEEE Access

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“…It is one of the most widely used analytical tools with high prediction accuracy. This algorithm is superior to several other classical algorithms because of its ease of implementation, efficiency when working with complex datasets, and ability to handle datasets with varying sample sizes (30).…”

Section: Resultsmentioning

confidence: 99%

Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study

Farshid,

Mirnia,

Rezaei-Hachesu

et al. 2023

IJRM

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Background: One of the major challenges that hospitals and clinicians face is the early identification of newborns at risk for adverse events. One of them is neonatal respiratory distress syndrome (RDS). RDS is the widest spared respiratory disorder in immature newborns and the main source of death among them. Machine learning has been broadly accepted and used in various scopes to analyze medical information and is very useful in the early detection of RDS. Objective: This study aimed to develop a model to predict neonatal RDS and affecting factors using data mining. Materials and Methods: The original dataset in this cross-sectional study was extracted from the medical records of newborns diagnosed with RDS from July 2017-July 2018 in Alzahra hospital, Tabriz, Iran. This data includes information about 1469 neonates, and their mothers information. The data were preprocessed and applied to expand the classification model using machine learning techniques such as support vector machine, Naïve Bayes, classification tree, random forest, CN2 rule induction, and neural network, for prediction of RDS episodes. The study compares models according to their accuracy. Results: Among the obtained results, an accuracy of 0.815, sensitivity of 0.802, specificity of 0.812, and area under the curve of 0.843 was the best output using random forest. Conclusion: The findings of our study proved that new approaches, such as data mining, may support medical decisions, improving diagnosis in neonatal RDS. The feasibility of using a random forest in neonatal RDS prediction would offer the possibility to decrease postpartum complications of neonatal care. Key words: Data mining, Classification, Neonatal respiratory distress syndrome, Newborn, Machine learning.

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“…We chose to use RF algorithm by using the LazyPredict package which engenders several ML algorithms and identifies the best performing one according to the specific dataset. Notably, RF has been already used in other studies investigating classification of subject based on pathology, resulting in excellent classification performances [ 25 , 74 , 75 , 76 , 77 , 78 ].…”

Section: Discussionmentioning

confidence: 99%

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

Trabassi,

Castiglia,

Bini

et al. 2024

Sensors

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The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.

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Classification of pathological disorders in children using random forest algorithm

Cited by 25 publications

References 21 publications

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

DeepMist: Toward Deep Learning Assisted Mist Computing Framework for Managing Healthcare Big Data

Developing a model to predict neonatal respiratory distress syndrome and affecting factors using data mining: A cross-sectional study

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

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