Prediction of Chronic Kidney Disease using SVM and CNN

Chronic kidney disease is one of the deadliest diseases today and it is vital to have a good diagnosis as soon as possible. In medical treatment, machine learning has been reported to be effective. A doctor can diagnose the disease early by using machine learning classifier algorithms. This study investigated the chronic disease prognosis of this concept. Disease data was taken from the University of California, Irvine. Other measurement algorithms used in this study include C5.0, Chi-square automatic interaction detector, line extraction, SVM line with L1 and L2 flap, and neural network random tree. The database was also submitted to a feature selection program that merited the database. Scores are computer generated for each category segment using the following methods: Full Version, (ii) Link-Based Feature Selection, (iii) Folder Feature Selection, (iv) Minimal Collapse and Selected Optional Retrospective Features, (v) integrated small oversampling method with very small reduction features and selected bias on the selected operator, and (vi) how to do multiple samples combined with full functions. In the full multi-sample processing process, the findings show that L2-loaded LSVM has a very high accuracy of 96.86 percent. The graph shows the results of different methods, as well as precision, precision, recall, F-score, area under the curve, and GINI coefficient. The minimum absolute reduction and selection regression operation selected features using the synthetic minority oversampling approach produced the best results after using the synthetic minority oversampling method with full features. The support vector machine achieved a high accuracy of 96.46 percent in the process of making very large samples with very small turndowns and selected operator features. Machine learning methods used with convolutional neural networks and SVM classifier models on the same database, with 96.7 percent of high-definition support machine models and networks are used

Implementation of Discretisation and Correlation-based Feature Selection to Optimize Support Vector Machine in Diagnosis of Chronic Kidney Disease

This study aims to improve the accuracy of the classification algorithm for diagnosing chronic kidney disease. There are several models of data mining. In classification, the Support Vector Machine (SVM) algorithm is widely used by researchers worldwide. The data used is a chronic kidney disease dataset taken from the UCI machine learning repository. This data consists of 25 attributes and 11 numeric data attributes, and 14 negative attributes. To call continuously, discrete data is used. Meanwhile, data is selected using Correlation-based Feature Selection (CFS) to reduce irrelevant and redundant data. The research results by applying discretization and feature selection based on correlation for classification in the SVM algorithm with 10-fold cross-validation show an increase in accuracy of 0.5%. The classification of the vector machine support algorithm in the diagnosis of chronic kidney disease produces an accuracy of 99.25%, and after applying discretization and correlation-based feature selection, produces an accuracy of 99.75%. Implementation of discretion and correlation-based feature selection to optimize support vector machine for diagnosis of chronic kidney disease has increased accuracy by 0.5%. The proposed method is feasible as a method of diagnosing chronic kidney disease

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

Multimodal Machine Learning for Automated ICD Coding

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.Comment: Machine Learning for Healthcare 201

Enhancing Accuracy on Chronic-Kidney Disease Detection Using Machine Learning with Technique of Resampling and Missing Value Treatment

Chronic kidney disease is one of the deadliest diseases in the world. It is important to identify chronic kidney disease at an early stage, so that treatment and prevention can be carried out early. This study used linear interpolation method to treat the missing values, resampling using SMOTE method, and several feature selection methods, such as Pearson’s correlation coefficient and Principal component analysis. For the classification methods, Support Vector Machine and Logistic Regression were used to build prediction models for chronic kidney disease based on dataset on UCI Machine Learning. To measure the performance of the model, several test scenarios were tested out so it can be compared to the previous research on the detection of chronic kidney disease, which is used as a benchmark for this study. The best result from the experiment is obtained from the scenario of resampling using SMOTE and feature selection using Principal Component Analysis with averaged accuracy, precision, and f1-score respectively are 98,8%, 100%, dan 98,77%

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

Performance Study Of Uncertainty Based Feature Selection Method On Detection Of Chronic Kidney Disease With SVM Classification

Chronic Kidney Disease (CKD) is a disorder that impairs kidney function. Early signs of CKD patients are very difficult until they lose 25% of their kidney function. Therefore, early detection and effective treatment are needed to reduce the mortality rate of CKD sufferers. In this study, the authors diagnose the CKD dataset using the Support Vector Machine (SVM) classification method to obtain accurate diagnostic results. The authors propose a comparison of the result on applying the feature selec- tion method to get the best feature candidates in improving the classification result. The testing process compares the Symmetrical Uncertainty (SU) and Multivariate Symmetrical Uncertainty (MSU) feature selection method and the SVM method as a classification method. Several experimental scenarios were carried out using the SU and MSU feature selection methods using the CKD dataset. From the results of the tests carried out, it shows that using the MSU feature selection method with 80%: 20% data split produces nine important features with an accuracy value of 0.9, sensi- tivity 0.84, specification 1.0, and when viewed on the ROC graph, the MSU method graph shows the true positive value is higher than the false positive value. So the classification using the MSU feature selection method is better than the SU feature selection method by 90% accurac