Machine Learning-based Diagnosis Research Articles

A machine learning-based diagnosis modeling of IgG4 Hashimoto's thyroiditis.

This study aims to develop a non-invasive diagnosis model using machine learning (ML) for identifying high-risk IgG4 Hashimoto's thyroiditis (HT) patients. A retrospective cohort of 93 HT patients and a prospective cohort of 179 HT patients were collected. According to the immunohistochemical and pathological results, the patients were divided into IgG4 HT group and non-IgG4 HT group. Serum TgAb IgG4 and TPOAb IgG4 were detected by ELISAs. A logistic regression model, support vector machine (SVM) and random forest (RF) were used to establish a clinical diagnosis model for IgG4 HT. Among these 272 patients, 40 (14.7%) were diagnosed with IgG4 HT. Patients with IgG4 HT were younger than those with non-IgG4 HT (P < 0.05). Serum levels of TgAb IgG4 and TPOAb IgG4 in IgG4 HT group were significantly higher than those in non-IgG4 HT group (P < 0.05). There were no significant differences in gender, disease duration, goiter, preoperative thyroid function status, preoperative TgAb or TPOAb levels, and thyroid ultrasound characteristics between the two groups (all P > 0.05). The accuracy, sensitivity, and specificity were 57%, 78%, and 79% for logistic regression model of IgG4 HT, 80 ± 7%, 84.7% ± 2.6%, and 75.4% ± 9.6% for the RF model and 78 ± 5%, 89.8% ± 5.7%, and 64.7% ± 5.7% for the SVM model. The RF model works better than SVM. The area under the ROC curve of RF ranged 0.87 to 0.92. A clinical diagnosis model for IgG4 HT established by RF model might help the early recognition of the high-risk patients of IgG4 HT.

Machine Learning-Based Diagnosis of Thermal Barrier Coating Process Quality

Machine Learning-Based Diagnosis of Thermal Barrier Coating Process Quality

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls.

This study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls. A total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set. The final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients. The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.

TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images

AbstractTuberculosis (TB) is a chronic infectious lung disease, which caused the death of about 1.5 million people in 2020 alone. Therefore, it is important to detect TB accurately at an early stage to prevent the infection and associated deaths. Chest X-ray (CXR) is the most popularly used method for TB diagnosis. However, it is difficult to identify TB from CXR images in the early stage, which leads to time-consuming and expensive treatments. Moreover, due to the increase of drug-resistant tuberculosis, the disease becomes more challenging in recent years. In this work, a novel deep learning-based framework is proposed to reliably and automatically distinguish TB, non-TB (other lung infections), and healthy patients using a dataset of 40,000 CXR images. Moreover, a stacking machine learning-based diagnosis of drug-resistant TB using 3037 CXR images of TB patients is implemented. The largest drug-resistant TB dataset will be released to develop a machine learning model for drug-resistant TB detection and stratification. Besides, Score-CAM-based visualization technique was used to make the model interpretable to see where the best performing model learns from in classifying the image. The proposed approach shows an accuracy of 93.32% for the classification of TB, non-TB, and healthy patients on the largest dataset while around 87.48% and 79.59% accuracy for binary classification (drug-resistant vs drug-sensitive TB), and three-class classification (multi-drug resistant (MDR), extreme drug-resistant (XDR), and sensitive TB), respectively, which is the best reported result compared to the literature. The proposed solution can make fast and reliable detection of TB and drug-resistant TB from chest X-rays, which can help in reducing disease complications and spread.

Lightweight Machine Learning-based Diagnosis for Power Electronic Systems Subject to Imbalanced Data

Lightweight Machine Learning-based Diagnosis for Power Electronic Systems Subject to Imbalanced Data

Machine learning-based diagnosis in wave power plants for cost reduction using real measured experimental data: Mutriku Wave Power Plant

In comparison to wind farms, the relative scarcity of actual operational data from wave power plants has contributed to a significant research gap in the areas of wave farm forecasting and cost reduction. In this context, this manuscript presents a new Machine Learning-based Power Take-Off (PTO) diagnosis for wave energy generation farms which has the potential to serve as an extensive reference for other wave energy farms and offer substantial benefits to both investors and policymakers involved in the advancement of the emerging wave technologies. The suggested method has been employed at the Mutriku Wave Power Plant (WWP) to facilitate the implementation of predictive maintenance strategies and reduce the Levelized Cost of Energy (LCoE). Hence, the research study considers two main extraction methods, namely, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), used to select the most relevant features for OWC diagnosis. In addition, two classification methods have been considered: Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN). The obtained data show that, although both methods allow to achieve an effective performance with an excellent degree of accuracy, the ANN-based method presents better results with 98% accuracy against 81% for the SVM when using PCA extraction method. Then, the developed classification-based OWC diagnosis has been used for the development of a predictive maintenance strategy at the Mutriku WPP, analyzing its impact on the economic indicators. The results indicate that, using the proposed predictive maintenance strategy, the OpEx may be decreased down to 17%, downtime may be decreased down to 55% and plant availability may be better up to 95%, leading to a 5% LCoE reduction.

Automated Machine Learning-Based Diagnosis and Molecular Characterization of Acute Leukemias using Flow Cytometry Data

Abstract Current flow cytometric analysis of blood and bone marrow samples for the diagnosis of acute leukemias relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into the resulting diagnosis and increasing diagnostic turn-around time. Additionally, concurrent molecular characterization of these samples via cytogenetics and targeted sequencing panels can take multiple days, thereby delaying patient diagnosis and treatment. Attention-based multi-instance learning models are machine learning models that can make accurate predictions and generate interpretable insights regarding the classification of a sample from multiple events/cells; while these models have been developed for anatomic pathology applications, they have yet to be applied to flow cytometry data. By utilizing 1,820 flow cytometry samples from 2019-2022 at Brigham and Women’s Hospital, we developed attention-based multi-instance machine learning models for automated diagnosis of acute leukemia, including differentiation of acute myeloid leukemia (AML) from B-lymphoblastic leukemia/lymphoma (B-ALL). Additionally, using concurrent cytogenetic and targeted sequencing data from 674 acute leukemia samples, machine learning models for prediction of molecular aberrancies from flow cytometry data were developed. Machine learning models were created using the TabNet deep learning architecture and VIME self-supervised training algorithm, which are state-of-the-art approaches towards machine learning from tabular data including flow cytometry data. Attention-based multi-instance models demonstrated strong performance for the automated diagnosis of acute leukemia versus non-leukemia samples (AUROC 0.869), as well as the separation of AML from B-ALL samples (AUROC 0.971). These models also accurately predicted cytogenetic aberrancies among AML samples, including t(15;17);PML::RARA (AUROC 1.00), as well as mutations including NPM1 (AUROC 0.725). These models do not require any manual intervention including compensation or gating, and additionally provide quantitative scores for the relative importance of different flow cytometry events and markers for the diagnosis of a particular sample. These importance scores can be integrated into flow cytometry analysis software for visualization and interpretation by hematopathologists. In this study, we have demonstrated the capability of machine learning models to provide automated diagnoses of acute leukemia, as well as accurately predict cytogenetic and molecular aberrancies in blood and bone marrow samples using flow cytometry data. This automated workflow can significantly decrease diagnostic turn-around time and ultimately improve patient outcomes.

Machine learning-based diagnosis of eye-diseases

&lt;span&gt;Over the last several years, artificial intelligence (AI) has been substantially utilized in image processing and classification. Several tools are accessible for visualizing, training, and pre-processing image data. One such tool is orange, which has several pre-processing modules and a particular add-on for image processing methods in addition to excellent data visualization. The tool (version 3.32.0) was used in the suggested study to give a comparative and predictive analysis using several classification models. Three main models have been used to train and predict the three groups image eye diseases. The results were compared based on some criteria, including area-under-a-curve (AUC), the accuracy of classification (CA), F1 score, precision, and recall. These models include K-nearest neighbour (KNN), logistic regression (LR), artificial neural networks (ANN) and stacking model. The stacking model, which is a novel model, is also presented in this work by concatenating the output of the parallel form of ANN and KNN models with the LR model. The best performance belonged to the Stacking model, which offers the best detection and prediction results.&lt;/span&gt;

Machine Learning-Based Diagnosis and Genetic Analysis of C9ORF72-Associated Amyotrophic Lateral Sclerosis (ALS)

Abstract: Amyotrophic Lateral Sclerosis (ALS), commonly known as Lou Gehrig's disease, is a devastating neurodegenerative disorder characterized by motor neuron degeneration. This research article explores the use of machine learning algorithms to diagnose ALS by analyzing mutations and insertions/deletions (indels) in the C9orf72 gene. The study utilizes a dataset to predict three stages of ALS: normal, intermediate risk, and full mutation. Three machine learning algorithms, Random Forest, SVM, and k-NN, were employed for diagnosis. Results indicate high accuracy in identifying ALS stages, with Random Forest and SVM achieving perfect accuracy when considering mutations in the Allele2. The findings demonstrate the potential of machine learning in early ALS diagnosis, offering insights for improved patient care and genetic understanding of the disease.

Machine learning-based diagnosis and risk classification of coronary artery disease using myocardial perfusion imaging SPECT: A radiomics study

This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models’ evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.

Automatic statistical diagnosis of COVID-19 based on multi-modal CT feature extraction

Background and purposeComputed tomography (CT) is highly sensitive to lung-related abnormalities as a non-invasive method and has become an essential tool for screening and diagnosing Coronavirus disease 2019 (COVID-19). To reduce the stress of work for physicians and speed up diagnosis, we propose a novel automatic diagnosis pipeline for COVID-19 based on high-dimensional radiomic features extracted from multimodal CT scans (multi-geometric and multiscale). Materials and methodsThere are 746 CT scans involved in this study, where 349 CT scans are COVID-19 positive and 397 CT scans are COVID-19 negative. All of them are from the public dataset. We first construct a transfer learning-based auto-segmentation model with a morphological post-processing block to improve the lung region segmentation. Then the radiomics feature extraction is guided by the proposed multi-modal CT scans strategy. In addition, our automatic diagnosis pipeline is driven by a well-designed loss function. We also explain the diagnosis capability from the related theory of linear subspace spanned by multi-modal radiomics features. ResultsUnder the 10-fold cross-validation strategy, our approach can achieve an improvement in diagnostic performance of 5. 77%, 7. 78%, 7. 74%, 7. 78%, 7. 45% compared to the radiomic features extracted from the original CT scans, and diagnosis performance is promoted to 91.53%, 86.46%, 86.47%, 86.46%, 86.95% in terms of AUC, Acc, F1, Recall and Precision in public datasets. ConclusionsWe demonstrate a statistically significant improvement of the proposed statistical learning method compared to the state-of-the-art machine learning-based diagnosis approaches. Thanks to theoretical support and excellent diagnostic performance, our method can be deployed in clinical auxiliary diagnosis, releasing the overstretched medical resources.

Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India.

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier's importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy.

Low tensor train and low multilinear rank approximations of 3D tensors for compression and de-speckling of optical coherence tomography images

Objective. Many methods for compression and/or de-speckling of 3D optical coherence tomography (OCT) images operate on a slice-by-slice basis and, consequently, ignore spatial relations between the B-scans. Thus, we develop compression ratio (CR)-constrained low tensor train (TT)—and low multilinear (ML) rank approximations of 3D tensors for compression and de-speckling of 3D OCT images. Due to inherent denoising mechanism of low-rank approximation, compressed image is often even of better quality than the raw image it is based on. Approach. We formulate CR-constrained low rank approximations of 3D tensor as parallel non-convex non-smooth optimization problems implemented by alternating direction method of multipliers of unfolded tensors. In contrast to patch- and sparsity-based OCT image compression methods, proposed approach does not require clean images for dictionary learning, enables CR as high as 60:1, and it is fast. In contrast to deep networks based OCT image compression, proposed approach is training free and does not require any supervised data pre-processing. Main results. Proposed methodology is evaluated on twenty four images of a retina acquired on Topcon 3D OCT-1000 scanner, and twenty images of a retina acquired on Big Vision BV1000 3D OCT scanner. For the first dataset, statistical significance analysis shows that for CR ≤ 35, all low ML rank approximations and Schatten-0 (S 0) norm constrained low TT rank approximation can be useful for machine learning-based diagnostics by using segmented retina layers. Also for CR ≤ 35, S 0-constrained ML rank approximation and S 0-constrained low TT rank approximation can be useful for visual inspection-based diagnostics. For the second dataset, statistical significance analysis shows that for CR ≤ 60 all low ML rank approximations as well as S 0 and S 1/2 low TT ranks approximations can be useful for machine learning-based diagnostics by using segmented retina layers. Also, for CR ≤ 60, low ML rank approximations constrained with S p , p ∊ {0, 1/2, 2/3} and one surrogate of S 0 can be useful for visual inspection-based diagnostics. That is also true for low TT rank approximations constrained with S p , p ∊ {0, 1/2, 2/3} for CR ≤ 20. Significance. Studies conducted on datasets acquired by two different types of scanners confirmed capabilities of proposed framework that, for a wide range of CRs, yields de-speckled 3D OCT images suitable for clinical data archiving and remote consultation, for visual inspection-based diagnosis and for machine learning-based diagnosis by using segmented retina layers.

Cholesterol Contributes to Risk, Severity, and Machine Learning-Driven Diagnosis of Lyme Disease.

Lyme disease is the most prevalent vector-borne disease in the US, yet its host factors are poorly understood and diagnostic tests are limited. We evaluated patients in a large health system to uncover cholesterol's role in the susceptibility, severity, and machine learning-based diagnosis of Lyme disease. A longitudinal health system cohort comprised 1 019 175 individuals with electronic health record data and 50 329 with linked genetic data. Associations of blood cholesterol level, cholesterol genetic scores comprising common genetic variants, and burden of rare loss-of-function (LoF) variants in cholesterol metabolism genes with Lyme disease were investigated. A portable machine learning model was constructed and tested to predict Lyme disease using routine lipid and clinical measurements. There were 3832 cases of Lyme disease. Increasing cholesterol was associated with greater risk of Lyme disease and hypercholesterolemia was more prevalent in Lyme disease cases than in controls. Cholesterol genetic scores and rare LoF variants in CD36 and LDLR were associated with Lyme disease risk. Serological profiling of cases revealed parallel trajectories of rising cholesterol and immunoglobulin levels over the disease course, including marked increases in individuals with LoF variants and high cholesterol genetic scores. The machine learning model predicted Lyme disease solely using routine lipid panel, blood count, and metabolic measurements. These results demonstrate the value of large-scale genetic and clinical data to reveal host factors underlying infectious disease biology, risk, and prognosis and the potential for their clinical translation to machine learning diagnostics that do not need specialized assays.

Pulmonary Tuberculosis Detection from Chest X-Ray Images Using Machine Learning

Abstract: One of today's deadliest diseases, tuberculosis (TB), is caused by "mycobacterium tuberculosis", which usually targets the lungs, due to weakening of immune system. Tuberculosis is very common, and if it is not detected, the patient's risk of death increases over time. Several computer diagnostic methods have been proposed to diagnose tuberculosis based on chest X-rays, as machine learning has been widely used in the field of image processing, especially deep learning. Adapting these machine learning techniques can provide more accurate, timely and reliable diagnostic results. Current research shows that manual diagnosis can be replaced by machine learning-based diagnosis with properly trained models, which provide more accurate results. DIP is becoming more prominent in the field of biomedicine. With image processing, a Support Vector Machine (SVM) learning model can be used to classify disabled lungs. The main objective of this paper is, to diagnose tuberculosis using machine learning trained models with chest x-ray images.

A machine learning-based diagnosis modelling of type 2 diabetes mellitus with environmental metal exposure

Background and Objective:Increasing and compelling evidence has been proved that urinary and dietary metal exposure are underappreciated but potentially modifiable biomarkers for type 2 diabetes mellitus (T2DM). The aims of this study were (1) to identify the key potential biomarkers which contributed to T2DM with effective and parsimonious features and (2) to assess the utility of baseline variables and metal exposure in the diagnosis of T2DM.Methods:Based on the National Health and Nutrition Examination Survey (NHANES), we selected 9822 screening records with 82 significant variables covering demographics, lifestyle, anthropometric measures, diet and metal exposure for this study. Combining extreme gradient boosting (XGBoost), random forest and light gradient boosting machine (lightGBM), a soft voting ensemble model was proposed to measure the importance of 82 features. With this soft voting ensemble model and variance inflation factor (VIF), strong multicollinear features with low importance scores were further removed from candidate biomarkers. Then, a soft voting ensemble classifier was adopted to demonstrate the efficiency of the proposed feature selection method.Results:With the novel feature selection method, 12 baseline variables and 3 metal variables were selected to detect patients at risk for T2DM in our study. For metal variables, the dietary copper (Cu), urinary cadmium (Cd) and urinary mercury (Hg) metals were selected as the most remarkable metal exposure and the corresponding P-values were all less than 0.05. In a classification model of T2DM with 12 baseline biomarkers, the addition of 3 metal exposure improved the classification accuracy of T2DM from a traditional area under the curve (AUC) 0.792 of the receiver operating characteristic (ROC) to an AUC 0.847.Conclusions:This was the first demonstration of T2DM classification with machine learning under urinary and dietary metal exposure. Improved prediction precision illustrated the effectiveness of the proposed machine learning-based diagnosis model facilitated lifestyle/dietary intervention for T2DM prevention.

Application of Computer Vision in Machine Learning-Based Diagnosis of Water Production Mechanisms in Oil Wells

Summary Diagnostic plots, introduced by K. S. Chan, are widely used to determine excessive water production mechanisms. In this paper, we introduce a computer vision model that is capable of segmenting and identifying multiple Chan signatures per plot, for the sake of surveillance and early screening, given that wells could exhibit diverse mechanisms throughout their life cycle. As deep learning (DL) demands a vast amount of information, we start our workflow by building a data set of 10,000 publicly available oil wells that have experienced varying water production mechanisms. Next, we perform preprocessing and remove anomalies from production data, which could be deceptive in analysis. Then, we visualize Chan plots as images and annotate them. Thereafter, we split data set, carry out augmentation, and put data together to be used as input for a convolutional neural network (CNN) layer. Eventually, data are trained utilizing you only look once (YOLO)—a one-stage object detector, hyperparameters are tuned, and model performance is evaluated using mean average precision (mAP). The collected data from fields in Alaska and North Dakota represent oil wells that have been producing for decades. When working with wells that possess noisy production data, we recognized challenge, bias, and tedium in human interpretation of Chan plots. Subsequently, we observed the inevitability of cleaning well production data before constructing the plots and thoroughly revealed its effect on enhancing model potentiality to get a fair score. In addition, we concluded that following a systematic approach of active learning, a technique that allows user to analyze mistakes of algorithm predictions and label data accordingly, accomplished a significant boost in model performance, especially with underrepresented classes. The proposed CNN model, which uses automatic feature extraction and expresses data in detail, is presumed to be robust as it successfully predicted multiple mechanisms of excessive water production, with confidence scores higher than 80%, in wells that exhibit different production conditions such as horizontal trajectories, artificial lift, waterflooding, stimulation, and other well intervention events. In this work, we introduce a novel computer vision model, which combines image processing and DL techniques to identify multiple water production signatures that a well can undergo and eliminate subjectivity of human interpretation. This approach has the potential to be effective, as a part of workflow automation, in expeditious surveillance of large oil fields. Source code is available on GitHub for public use.

Machine learning-based diagnosis of breast cancer utilizing feature optimization technique

Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react.

A Machine Learning-Based Diagnosis Modelling of Type 2 Diabetes with Environmental Metal Exposure

A Machine Learning-Based Diagnosis Modelling of Type 2 Diabetes with Environmental Metal Exposure

Machine Learning-based Diagnosis of Autism Spectrum Disorder Using Brain Imaging

In spite of research over the past decades, robust, replicable, and clinically translatable markers to objectively diagnose psychiatric disorders are yet to be ascertained. Several factors such as biological heterogeneity (partly due to the complex genetic basis that is further compounded by environmental interactions), the potential mismatch between contemporary diagnostic criteria / clinical symptom scores and findings that emanate from cutting-edge neuroscience observations, and similar others have made the identification of biomarkers a daunting challenge. This challenge becomes much harder to solve in the context of disorders of childhood onset such as, autism spectrum disorders (ASD) especially because of the additional complexity of examining the developing brain.