A Comparative Analysis of Supervised Classification Algorithms and Missing Data Handling for Enhancing Chronic Kidney Disease Prediction

Chronic kidney disease (CKD), which is becoming a more significant public health concern, is characterized by a gradual but concerning increase in morbidity and death, particularly in its early, asymptomatic stages. Risk factors for chronic kidney disease (CKD), including genetic predisposition, obesity, diabetes, and hypertension, affect the illness's prevalence. When there are no outward signs of an illness, it is challenging to diagnose and treat it in its early stages. To tackle this pressing issue, our research does a comprehensive investigation through a comparative comparison of supervised classification techniques. In particular, we examine the prediction performance of CKD using the Random Forest, Decision Tree, and Support Vector Machine (SVM) techniques. We also look into a number of approaches to handling missing data. Our research presents a thorough evaluation of these algorithms' performance under different data cleaning methods, pointing out both their benefits and drawbacks. Ultimately, our research aims to clarify the early detection and treatment of chronic kidney disease (CKD) and pave the way for larger-scale public health initiatives to tackle this quickly escalating health emergency

Automated Chronic Kidney Disease Detection Model with Knearest Neighbor

Chronic kidney disease is one of the most common disease in the world today. Kidney disease causes death if the patient is not threated at early stage. One of the challenge in kidney disease treatment is accurate identification of kidney disease at an early stage. Moreover, detecting kidney disease requires experienced nephrologist. However, in developing nations lack of medical specialist or nephrologist for identifying chronic kidney disease makes the problem more challenging. As alternative solution to kidney disease identification, researchers have developed many intelligent models using K-nearest Neighbors (KNN) algorithm. However, the accuracy of the existing KNN model has scope for improvement. Thus, this study proposed KNN based model for accurate identification of kidney disease at early stage. To develop optimized KNN model, we have employed error plot to find most favorable K value to obtain more accurate result than the existing models. To conduct experiments, study employed kidney disease dataset collected form publically available Kaggle data repository for training and testing the proposed model. Finally, we have evaluated the proposed model against predictive accuracy. The experimental result on the proposed model appears to prove that the predictive accuracy of the model is 99.86%

IMPROVING THE PERFORMANCE OF SUPPORT VECTOR MACHINE WITH FORWARD SELECTION FOR PREDICTION OF CHRONIC KIDNEY DISEASE

Chronic kidney disease is a disorder that affects the kidneys and arises due to various factors. Chronic kidney disease, usually develops slowly and is chronic. For prevention and control, proper treatment is needed, so that detection of this disease can play a very important role. This study aims to determine the level of accuracy in predicting chronic kidney disease through SVM based on forward selection and to determine the performance of Feature Selection which is applied to the SVM method in solving problems in chronic kidney disease. This research was conducted an experiment on the SVM method using various kinds of kernels and it was seen that SVM with the dot kernel was 98.50% with AUC 1,000 which was superior to the polynominal kernel and RBF. However, when the experiment was carried out again by applying FS to SVM, it was found that SVM+FS with the RBF kernel outperformed the other kernels by 99.75% with AUC 1,000. So it can be concluded that the Forward Selection of SVM has succeeded in improving its performance, especially in this case, namely the prediction of chronic kidney diseas

Common human diseases prediction using machine learning based on survey data

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.Comment: 11 pages, 6 figures, accepted in Bulletin of Electrical Engineering and Informatics Journa

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

Applying Machine Learning Algorithms for the Analysis of Biological Sequences and Medical Records

The modern sequencing technology revolutionizes the genomic research and triggers explosive growth of DNA, RNA, and protein sequences. How to infer the structure and function from biological sequences is a fundamentally important task in genomics and proteomics fields. With the development of statistical and machine learning methods, an integrated and user-friendly tool containing the state-of-the-art data mining methods are needed. Here, we propose SeqFea-Learn, a comprehensive Python pipeline that integrating multiple steps: feature extraction, dimensionality reduction, feature selection, predicting model constructions based on machine learning and deep learning approaches to analyze sequences. We used enhancers, RNA N6- methyladenosine sites and protein-protein interactions datasets to evaluate the validation of the tool. The results show that the tool can effectively perform biological sequence analysis and classification tasks. Applying machine learning algorithms for Electronic medical record (EMR) data analysis is also included in this dissertation. Chronic kidney disease (CKD) is prevalent across the world and well defined by an estimated glomerular filtration rate (eGFR). The progression of kidney disease can be predicted if future eGFR can be accurately estimated using predictive analytics. Thus, I present a prediction model of eGFR that was built using Random Forest regression. The dataset includes demographic, clinical and laboratory information from a regional primary health care clinic. The final model included eGFR, age, gender, body mass index (BMI), obesity, hypertension, and diabetes, which achieved a mean coefficient of determination of 0.95. The estimated eGFRs were used to classify patients into CKD stages with high macro-averaged and micro-averaged metrics

A Robust Intuitionistic Fuzzy Constraint Score based Potential Feature Subset Selection for Chronic Diseases Detection

This work proposes a novel feature selection algorithm for high-dimensional features in real-time datasets for prediction or classification. Conventional methods assume dataset values as crisp formats, but in real datasets, instances are represented in linguistic formats, requiring the use of uncertainty theories. The Intuitionistic Fuzzy Similarity based constraint score is proposed, where each feature is denoted as an independent variable and the class variable as a dependent variable. The features are represented in triplet form, with grade of belongingness, non-belongingness, and hesitancy index to maximize relevancy and reduce redundancy. Pairwise similarity matching is computed using Intuitionistic fuzzy similarity distance measure for supervised learning and intuitionistic fuzzy K-NN for semi-supervised learning. Potential feature subsets are selected and validated using deep learning algorithms. The results show that the proposed Intuitionistic fuzzy Constraint score feature selection algorithm produces optimal results compared to other state-of-the-art methods in chronic disease prediction

Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques

Applying data mining and machine learning (ML) techniques to clinical data might identify predictive biomarkers for diabetic nephropathy (DN), a common complication of type 2 diabetes mellitus (T2DM). A retrospective analysis of the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was intended to identify such factors using ML. The longitudinal data were stratified by time after patient enrollment to differentiate early and late predictors. Our results showed that Random Forest and Simple Logistic Regression methods exhibited the best performance among the evaluated algorithms. Baseline values for glomerular filtration rate (GFR), urinary creatinine, urinary albumin, potassium, cholesterol, low-density lipoprotein, and urinary albumin to creatinine ratio were identified as DN predictors. Early predictors were the baseline values of GFR, systolic blood pressure, as well as fasting plasma glucose (FPG) and potassium at month 4. Changes per year in GFR, FPG, and triglycerides were recognized as predictors of late development. In conclusion, ML-based methods successfully identified predictive factors for DN among patients with T2DM

Early detection of chronic kidney disease in low-income and middle-income countries: development and validation of a point-of-care screening strategy for India.

INTRODUCTION: Although deaths due to chronic kidney disease (CKD) have doubled over the past two decades, few data exist to inform screening strategies for early detection of CKD in low-income and middle-income countries. METHODS: Using data from three population-based surveys in India, we developed a prediction model to identify a target population that could benefit from further CKD testing, after an initial screening implemented during home health visits. Using data from one urban survey (n=8698), we applied stepwise logistic regression to test three models: one comprised of demographics, self-reported medical history, anthropometry and point-of-care (urine dipstick or capillary glucose) tests; one with demographics and self-reported medical history and one with anthropometry and point-of-care tests. The 'gold-standard' definition of CKD was an estimated glomerular filtration rate <60 mL/min/1.73 m2 or urine albumin-to-creatinine ratio ≥30 mg/g. Models were internally validated via bootstrap. The most parsimonious model with comparable performance was externally validated on distinct urban (n=5365) and rural (n=6173) Indian cohorts. RESULTS: A model with age, sex, waist circumference, body mass index and urine dipstick had a c-statistic of 0.76 (95% CI 0.75 to 0.78) for predicting need for further CKD testing, with external validation c-statistics of 0.74 and 0.70 in the urban and rural cohorts, respectively. At a probability cut-point of 0.09, sensitivity was 71% (95% CI 68% to 74%) and specificity was 70% (95% CI 69% to 71%). The model captured 71% of persons with CKD and 90% of persons at highest risk of complications from untreated CKD (ie, CKD stage 3A2 and above). CONCLUSION: A point-of-care CKD screening strategy using three simple measures can accurately identify high-risk persons who require confirmatory kidney function testing

Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review

In recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra- and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry