27 research outputs found
A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients
No full textCancer is a fatal disease that affects people of all ages, including children. It is one of the leading causes of death worldwide. According to World Health Organization, an estimated 400,000 children develop cancer yearly. Bone marrow transplantation (BMT) is a specialized treatment for patients suffering from certain types of cancer, such as myeloma, lymphoma, leukemia, and others. It usually includes extracting healthy cells from the donor’s bone marrow and replacing the existing ones in the patient’s body. However, the treatment can also cause complications such as graft-versus-host disease, organ damage, stem cell failure, new cancers, and infections. In this study, we use machine learning and explainable artificial intelligence (XAI) techniques to predict the survival rate of children undergoing Hematopoietic Stem Cell Transplants. Three feature selection techniques have been utilized for feature selection: Harris Hawks optimization, salp swarm optimization, and mutual information. The final custom stacked model delivered optimal results with accuracy, precision (89%), recall (88%), f1-score (88%), area under curve (AUC) (92%), and average precision (86%). In addition, XAI techniques such as Shapley additive values (SHAP), local interpretable model-agnostic explanations (LIME), ELI5, and QLattice have been used to make the models more precise, understandable, and interpretable. According to XAI, the most important features were relapse, donor age, recipient age, and platelet recovery time. The promising results point to the potential use of artificial intelligence in understanding the effectiveness of bone marrow transplants in children
Author Correction: An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients
No full textSupervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters
No full textThe World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases
Explainable machine learning methods to predict postpartum depression risk
No full textPostpartum depression (PPD) is a type of depression that mothers have following childbirth due to hormonal changes, psychological transition to parenting, and exhaustion. This depression strikes either during/or in the first year following childbirth. It is also a frequently disregarded medical condition that must be treated right away as it might have major repercussions. Machine learning (ML) and artificial intelligence (AI) are tools that healthcare professionals can utilize to anticipate this condition more rapidly and correctly. Consequently, we have demonstrated how to use explainable artificial intelligence (XAI) methods and heterogeneous classifiers to predict postpartum depression in mothers who have recently given birth. The K-Nearest Neighbor (KNN) model and the customized stack model outperformed all other classifiers. KNN model obtained 97% accuracy, 98% recall, and 95% precision and the stack model obtained 97% accuracy, 100% recall, and 94% precision, respectively. A set of frameworks and resources known as explainable artificial intelligence (XAI) facilitates the comprehension and interpretation of predictions made by machine learning algorithms. Four distinct XAI techniques: ELI5, Shapley Additive Values (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchor – have been used to interpret the model predictions. Explainability, interpretability, accountability, and transparency are crucial parameters of XAI, ensuring that machine learning models provide understandable and trustworthy results to users and stakeholders. The goal of this interdisciplinary research is to develop an automated diagnosis framework with tools that can transform therapy for postpartum depression leading to suicide attempts and empower medical professionals to offer mothers individualized, high-quality care
A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence
No full textThe coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse transcription-polymerase chain reaction (RT-PCR) test is the current golden standard for diagnosing different infectious diseases, including COVID-19; however, it is not always accurate. Therefore, it is extremely crucial to find an alternative diagnosis method which can support the results of the standard RT-PCR test. Hence, a decision support system has been proposed in this study that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic and blood markers. The patient data used in this research were collected from two Manipal hospitals in India and a custom-made, stacked, multi-level ensemble classifier has been used to predict the COVID-19 diagnosis. Deep learning techniques such as deep neural networks (DNN) and one-dimensional convolutional networks (1D-CNN) have also been utilized. Further, explainable artificial techniques (XAI) such as Shapley additive values (SHAP), ELI5, local interpretable model explainer (LIME), and QLattice have been used to make the models more precise and understandable. Among all of the algorithms, the multi-level stacked model obtained an excellent accuracy of 96%. The precision, recall, f1-score and AUC obtained were 94%, 95%, 94% and 98% respectively. The models can be used as a decision support system for the initial screening of coronavirus patients and can also help ease the existing burden on medical infrastructure
Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach
No full textThe human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson’s correlation techniques are utilized for feature selection. Since the data was imbalanced, the Borderline-SMOTE technique was used to balance the data. The final stacked machine learning model obtained an accuracy, precision, recall, F1-score, area under curve (AUC) and average precision of 98%, 97%, 99%, 98%, 100% and 100%, respectively. To make the model explainable and interpretable to clinicians, explainable artificial intelligence algorithms such as Shapley additive values (SHAP), local interpretable model agnostic explanation (LIME), random forest and ELI5 have been effectively utilized. The optimistic results indicate the potential of automated frameworks to assist doctors and medical professionals in diagnosing and screening potential cervical cancer patients
Predicting acute myocardial infarction from haematological markers utilizing machine learning and explainable artificial intelligence
No full textMyocardial infarction (MI) is the leading cause of human death globally. It occurs when a blockage in an artery prevents blood and oxygen from reaching the heart muscle, causing tissues in the heart muscle to die. This leads to a necessity to develop a method to diagnose MI’s early, preventing further complications such as irregular heart rhythm, heart failure or even cardiac arrest. This research aims to develop a more accurate machine learning (ML) model to help predict acute myocardial infarction (AMI) with a greater degree of accuracy without invasive procedures using additional explainable artificial intelligence (XAI) techniques which will help medical practitioners to better diagnose AMI more precisely. According to the results, the random forest classifier model gave the highest accuracy of 86%. XAI techniques were used to visualize the data and results, and determined white blood cell (WBC) count to be the most crucial feature in classification, followed by neutrophil (NEU) count, neutrophil-lymphocyte (NEU/LY) ratio, platelet width of distribution (PDW) values and basophil (BA) counts. The developed model can help medical practitioners make a more accurate early diagnosis of AMI using readily available hematological parameters, enabling practitioners to provide superior care to a diverse range of individuals
Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases
No full textThe mortality rate due to chronic pediatric respiratory diseases is increasing every year and it is important to assess the severity of these diseases. As symptoms of several pediatric respiratory disorders are frequently identical, identification might be difficult due to the ongoing spread of respiratory diseases. Large datasets of clinical variables are analyzed by machine learning (ML) to find patterns and co-relations that human clinicians might not be able to predict immediately. As a result, pediatric respiratory disease severity can be detected more quickly and accurately. The KBest feature selection method is used initially to get the best fifteen features from the dataset. The random forest classifier performed well with the best accuracy of 96% compared to other classifiers. Shapley Additive Values (SHAP), Explain Like I’m 5 (ELI5), QLattice, and Local Interpretable Model-agnostic Explanations (LIME) are four Explainable Artificial Intelligence (XAI) techniques used to interpret model predictions. The most significant attributes were patient transfer to the intensive care unit, Kaliemia, Creatinine Blood Test, Cyanosis, and Natremia. The promising results suggest integrating ML into pediatric respiratory disease diagnosis for predictive accuracy and improved patient outcomes
Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence
No full textStroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment
Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning
No full textKrishnaraj Chadaga, Chinmay Chakraborty, Srikanth Prabhu, Shashikiran Umakanth, Vivekananda Bhat，Niranjana SampathilaAbstractCoronavirus 2 (SARS-CoV-2), often known by the name COVID-19, is a type of acute respiratory syndrome that has had a significant influence on both economy and health infrastructure worldwide. This novel virus is diagnosed utilising a conventional method known as the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test. This approach, however, produces a lot of false-negative and erroneous outcomes. According to recent studies, COVID-19 can also be diagnosed using X-rays, CT scans, blood tests and cough sounds. In this article, we use blood tests and machine learning to predict the diagnosis of this deadly virus. We also present an extensive review of various existing machine-learning applications that diagnose COVID-19 from clinical and laboratory markers. Four different classifiers along with a technique called Synthetic Minority Oversampling Technique (SMOTE) were used for classification. Shapley Additive Explanations (SHAP) method was utilized to calculate the gravity of each feature and it was found that eosinophils, monocytes, leukocytes and platelets were the most critical blood parameters that distinguished COVID-19 infection for our dataset. These classifiers can be utilized in conjunction with RT-PCR tests to improve sensitivity and in emergency situations such as a pandemic outbreak that might happen due to new strains of the virus. The positive results indicate the prospective use of an automated framework that could help clinicians and medical personnel diagnose and screen patients.</p
