The Performance of Machine Learning for Chronic Kidney Disease Diagnosis

This paper aims to review the performance of different machine learning (ML) models and develop models for the automated diagnosis of chronic kidney disease. To detect chronic kidney disease with better precision, selecting the right and better-performing ML model is significant as it improves the precision and accuracy of the chronic kidney disease diagnosis. The study uses the Joana Briggs Institute (JBI) scoping review methodology, which involves different steps such as searching relevant literature, conducting the review, and reporting the review result. In the search, the year of publication and the indexing of journals where the studies are published is used as inclusion and exclusion criteria. The review result shows that the current chronic kidney disease detection has focused on the development of ensemble-based and deep-learning methods. The deep learning method can achieve a higher accuracy of 99.75%

ROLE OF MACHINE VISION FOR IDENTIFICATION OF KIDNEY STONES USING MULTI FEATURES ANALYSIS

The purpose of this study is to highlight the significance of machine vision for the Classification of kidney stone identification. A novel optimized fused texture features frame work was designed to identify the stones in kidney.  A fused 234 texture feature namely (GLCM, RLM and Histogram) feature set was acquired by each region of interest (ROI). It was observed that on each image 8 ROI’s of sizes (16x16, 20x20 and 22x22) were taken. It was difficult to handle a large feature space 280800 (1200x234). Now to overcome this data handling issue we have applied feature optimization technique namely POE+ACC and acquired 30 most optimized features set for each ROI. The optimized fused features data set 3600(1200x30) was used to four machine vision Classifiers that is Random Forest, MLP, j48 and Naïve Bayes. Finally, it was observed that Random Forest provides best results of 90% accuracy on ROI 22x22 among the above discussed deployed Classifier

The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.Comment: 8 page

Kidney Ailment Prediction under Data Imbalance

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases. In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related to class imbalance problem. This occurs when at least one of the classes are represented by significantly smaller number of samples than the others in the training set. The problem with imbalanced dataset is that the classifiers tend to classify all samples as majority class, ignoring the minority class samples. The second issue is on the specific type of data to be used for a given problem. Here, we focused on predicting kidney diseases based on patient information extracted from laboratory and questionnaire data. Most recent approaches for predicting kidney diseases or other chronic diseases rely on the usage of prescription drugs. In this study, we focus on biomarker and anthropometry data of patients to analyze and predict kidney-related diseases. In this research, we adopted a learning approach which involves repeated random data sub-sampling to tackle the class imbalance problem. This technique divides the samples into multiple sub-samples, while keeping each training sub-sample completely balanced. We then trained classification models on the balanced data to predict the risk of kidney failure. Further, we developed an intelligent fusion mechanism to combine information from both the biomarker and anthropometry data sets for improved prediction accuracy and stability. Results are included to demonstrate the performance

Meeting the challenge of diabetes in ageing and diverse populations: a review of the literature from the UK

The impact of type 2 diabetes on ageing societies is great and populations across the globe are becoming more diverse. Complications of diabetes unequally affect particular groups in the UK older people, and people with a South Asian background are two population groups with increased risk whose numbers will grow in the future. We explored the evidence about diabetes care for older people with South Asian ethnicity to understand the contexts and mechanisms behind interventions to reduce inequalities. We used a realist approach to review the literature, mapped the main areas where relevant evidence exists, and explored the concepts and mechanisms which underpinned interventions. From this we constructed a theoretical framework for a programme of research and put forward suggestions for what our analysis might mean to providers, researchers, and policy makers. Broad themes of cultural competency; comorbidities and stratification; and access emerged as mid-level mechanisms which have individualised, culturally intelligent, and ethical care at their heart and through which inequalities can be addressed. These provide a theoretical framework for future research to advance knowledge about concordance; culturally meaningful measures of depression and cognitive impairment; and care planning in different contexts which support effective diabetes care for aging and diverse populations

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively

U-Capkidnets++-: A Novel Hybrid Capsule Networks with Optimized Deep Feed Forward Networks for an Effective Classification of Kidney Tumours Using CT Kidney Images

Chronic Kidney Diseases (CKD) has become one among the world wide health crisis and needs the associated efforts to prevent the complete organ damage. A considerable research effort has been put forward onto the effective seperation and classification of kidney tumors from the kidney CT Images. Emerging machine learning along with deep learning algorithms have waved the novel paths of tumor detections. But these methods are proved to be laborious and its success rate is purely depends on the previous experiences. To achieve the better classification and segmentation of tumors, this paper proposes the hybrid ensemble of visual capsule networks in U-NET deep learning architecture and w deep feed-forward extreme learning machines. The proposed framework incorporates the data-preprocessing powerful data augmentation, saliency tumor segmentation (STS) followed by the classification phase. Furthermore, classification levels are constructed based upon the feed forward extreme learning machines (FFELM) to enhance the effectiveness of the suggested model .The extensive experimentation has been conducted to evaluate the efficacy of the recommended structure and matched with the other prevailing hybrid deep learning model. Experimentation demonstrates that the suggested model has showed the superior predominance over the other models and exhibited DICE co-efficient of kidney tumors as high as 0.96 and accuracy of 97.5 %respectively

Forecasting Chronic Kidney Disease Using Ensemble Machine Learning Technique

India is a rapidly expanding nation on a global scale. Chronic kidney disease (CKD) is a prevalent health problem internationally, and advance perception of this disease can aid prevent its stream. This research proposes an ensemble learning technique that combines three different algorithms, Logistic Regression, Gradient Boosting and Random Forest for the prediction of CKD. The performance of each algorithm was judged based on Root Mean Square Error (RMSE) and Mean Square Error (MSE) as performance metrics, and the predictions of each algorithm were combined using an ensemble learning technique. The dataset used for the study contained data on 400 individuals with 24 different features, which was pre-processed by removing missing values and normalizing the data. The combined algorithm showed a better performance with an RMSE of 0.2111 and an MSE of 0.0446, compared to individual algorithms. The proposed ensemble learning technique can be utilized as a divining for advance perception of CKD. The outcomes of the work reveal the effectiveness of the technique and its potential for improving patient outcomes by preventing the progression of CKD. Additionally, the ensemble learning technique can be applied to other predictive tasks to improve performance, indicating its broader applicability