COVID-19 Pandemic: Role of Machine Learning &amp; Deep Learning Methods in Diagnosis

Trivedi, Naresh Kumar; Simaiya, Sarita; Lilhore, Umesh Kumar; Sharma, Sanjeev Kumar

doi:10.31782/ijcrr.2021.sp192

Cited by 27 publications

(13 citation statements)

References 9 publications

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“…The allocation of patient data predicated on the dependent variable demonstrates that the original dataset is imbalanced. Across pre-processing phase, the SMOTE method has been used to tackle this problem 45 .…”

Section: Resultsmentioning

confidence: 99%

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Lilhore,

Manoharan,

Sandhu

et al. 2023

Sci Rep

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Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree’s minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a ‘Ranker method’ to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model’s performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.

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Section: Resultsmentioning

confidence: 99%

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Lilhore,

Manoharan,

Sandhu

et al. 2023

Sci Rep

Self Cite

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“…This layer effectively captures the clinical notes’ temporal relationships and contextual information. When dealing with audio data, extracting and normalizing specific characteristics, including Mel-frequency cepstral coefficients (MFCCs) 30 and spectrograms, is common practice. Figure 6 presents the feature co-relationship of textual features, and Fig.…”

Section: Methodsmentioning

confidence: 99%

“…TN represents true negatives, which are instances correctly identified as negative. FN denotes false negatives incorrectly identified as negative 30 . Confusion Matrix: A table is used to evaluate the efficacy of a machine and deep learning algorithm by comparing the predicted labels with the actual labels in a given dataset.…”

Section: Methodsmentioning

confidence: 99%

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Lilhore,

Dalal,

Varshney

et al. 2024

Sci Rep

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Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and ‘speech records’ of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

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“…These statistics are pre-processed to tackle missing value difficulties and generate accurate forecasts. The pre-processing data phase consists of several stages, including data cleaning, transformation (normalization and aggregation), data integration, and reduction [39].…”

Section: Pre-processing Of Heart Disease Datasetmentioning

confidence: 99%

Predictive Analysis of Heart Diseases With Machine Learning Approaches

Lilhore

Poongodi

et al. 2022

MJCS

Self Cite

160

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Machine Learning (ML) is used in healthcare sectors worldwide. ML methods help in the protection of heart diseases, locomotor disorders in the medical data set. The discovery of such essential data helps researchers gain valuable insight into how to utilize their diagnosis and treatment for a particular patient. Researchers use various Machine Learning methods to examine massive amounts of complex healthcare data, which aids healthcare professionals in predicting diseases. In this research, we are using an online UCI dataset with 303 rows and 76 properties. Approximately 14 of these 76 properties are selected for testing, which is necessary to validate the performances of different methods. The isolation forest approach uses the data set’s most essential qualities and metrics to standardize the information for better precision. This analysis is based on supervised learning methods, i.e., Naive Bayes, SVM, Logistic regression, Decision Tree Classifier, Random Forest, and K- Nearest Neighbor. The experimental results demonstrate the strength of KNN with eight neighbours order to test the effectiveness, sensitivity, precision, and accuracy, F1-score; as compared to other methods, i.e., Naive Bayes, SVM (Linear Kernel), Decision Tree Classifier with 4 or 18 features, and Random Forest classifiers.

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COVID-19 Pandemic: Role of Machine Learning & Deep Learning Methods in Diagnosis

Cited by 27 publications

References 9 publications

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Predictive Analysis of Heart Diseases With Machine Learning Approaches

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