Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in the precise assessment of chronic disorder risks in patients. Traditional attribute selection approaches suffer from premature convergence, high complexity, and computational cost. On the contrary, heuristic-based optimization to supervised methods minimizes the computational cost by eliminating outlier attributes. In this study, a novel buffer-enabled heuristic, a memory-based metaheuristic attribute selection (MMAS) model, is proposed, which performs a local neighborhood search for optimizing chronic disorders data. It is further filtered with unsupervised K-means clustering to remove outliers. The resultant data are input to the Naive Bayes classifier to determine chronic disease risks' presence. Heart disease, breast cancer, diabetes, and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall, and F-score metric computed were 96.05%, 94.07%, and 95.06%, respectively. The model also has a least latency of 0.8 sec. Thus, it is demonstrated that chronic disease diagnosis can be significantly improved by heuristic-based attribute selection coupled with clustering followed by classification. It can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner.

/pdf/a-decisive-metaheuristic-attribute-selector-enabled-combined-3ekwxb72.pdf

A Decisive Metaheuristic Attribute Selector Enabled Combined Unsupervised-Supervised Model for Chronic Disease Risk Assessment

Chronic Kidney Disease (CKD) is being typically observed as a health threatening issue, especially in developing countries, where receiving proper treatments are very expensive. Therefore, early prediction of CKD that protects the kidney and breaks the gradual progress of CKD has become an important issue for physicians and scientists. Internet of Things (IoT) as a useful paradigm in which, low cost body sensor and smart multimedia medical devices are applied to provide remote monitoring of kidney function, plays an important role, especially where the medical care centers are hardly available for most of people. To gain this objective, in this paper, a diagnostic prediction model for CKD and its severity is proposed that applies IoT multimedia data. Since the influencing features on CKD are enormous and also the volume of the IoT multimedia data is usually very huge, selecting different features based on physicians’ clinical observations and experiences and also previous studies for CKD in different groups of multimedia datasets is carried out to assess the performance measures of CKD prediction and its level determination via different classification techniques. The experimental results reveal that the applied dataset with the proposed selected features produces 97% accuracy, 99% sensitivity and 95% specificity via applying decision tree (J48) classifier in comparison to Support Vector Machine (SVM), Multi-Layer Perception (MLP) and Naive Bayes classifiers. Also, the proposed feature set can improve the execution time in comparison to other datasets with different features.

A diagnostic prediction model for chronic kidney disease in internet of things platform

Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models. This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models. Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The prediction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77. Incorporating machine learning into clinical outcome prediction using three key predictors including time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are most likely to benefit from contemporary task-oriented interventions. The KNN and ANN models may be potentially useful for predicting clinically significant motor recovery in chronic stroke.

/pdf/predicting-clinically-significant-motor-function-improvement-s0juikq5gu.pdf

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches.

Diabetes is a very common disease and beside it causes serious health problems such as fatal kidney damage or blindness; it may lead the patient to death. There is no exact cure for this disease yet but it is manageable with medication and diet. In this manner, importance of correct diagnosis of diabetes is very important to identify the diseases in early stage and take necessary precautions. There is a lot of data accumulated on this subject, as there are so many patients with this condition. This makes it possible for researchers to use data mining techniques on this subject. This study is proposed to classify diabetes by using data mining techniques. The dataset which has been obtained from UCI machine learning depository contains 520 instances, each having 17 attributes. Seven different classification algorithm including Bayes Network, Naive Bayes, J48, Random Tree, Random Forest, k-NN and SVM have been studied on this dataset. Obtained results indicated that k-NN performed the highest accuracy with 98.07% and this algorithm is the best method to identify and classify diabetes diseases on studies dataset.

Classification of Diabetes Dataset with Data Mining Techniques by Using WEKA Approach

In December 2019, SARS-CoV-2 caused coronavirus disease (COVID-19) distributed to all countries, infecting thousands of people and causing deaths. COVID-19 induces mild sickness in most cases, although it may render some people very ill. Therefore, vaccines are in various phases of clinical progress, and some of them being approved for national use. The current state reveals that there is a critical need for a quick and timely solution to the Covid-19 vaccine development. Non-clinical methods such as data mining and machine learning techniques may help do this. This study will focus on the COVID-19 World Vaccination Progress using Machine learning classification Algorithms. The findings of the paper show which algorithm is better for a given dataset. Weka is used to run tests on real-world data, and four output classification algorithms (Decision Tree, K-nearest neighbors, Random Tree, and Naive Bayes) are used to analyze and draw conclusions. The comparison is based on accuracy and performance period, and it was discovered that the Decision Tree outperforms other algorithms in terms of time and accuracy.

https://journal.qubahan.com/index.php/qaj/article/download/53/38

COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms

In recent years, the advent of latest web and data technologies has encouraged massive data growth in almost every sector. Businesses and leading industries are viewing these huge data repositories as a tool to design future strategies, prediction models by analyzing patterns and gaining knowledge from this unstructured data by applying different data mining techniques. Medical domain has now become richer in term of maintaining digital records of patients related to their diagnosis and treatment. These huge data repositories can range from patient personnel data, diagnosis, treatment histories, test diagnosis, images and various scans. This terabytes of medical data is quantity rich but weaker in information in terms of knowledge and robust tools to identify hidden patterns of knowledge specifically in medical sector. Data Mining as a field of research has already well proven capabilities of identifying hidden patterns, analysis and knowledge applied on different research domains, now gaining popularity day by day among researchers and scientist towards generating novel and deep insights of these large biomedical datasets also. Uncovering new biomedical and healthcare related knowledge in order to support clinical decision making, is another dimension of data mining. Through massive literature survey, it is found that early disease prediction is the most demanded area of research in health care sector. As health care domain is bit wider domain and having different disease characteristics, different techniques have their own prediction efficiencies, which can be enhanced and changed in order to get into most optimize way. In this research work, authors have comprehensively compared different data classification techniques and their prediction accuracy for chronic kidney disease. Authors have compared J48, Naive Bayes, Random Forest, SVM and k-NN classifiers using performance measures like ROC, kappa statistics, RMSE and MAE using WEKA tool. Authors have also compared these classifiers on various accuracy measures like TP rate, FP rate, precision, recall and f-measure by implementing on WEKA. Experimental result shows that random forest classifier has better classification accuracy over others for chronic kidney disease dataset.

Implementing WEKA for medical data classification and early disease prediction

Objective: To intend towards increasing classification efficiency of J48 classifier by introducing attribute set on the basis of applied genetic programming. The constructed set of attribute not only enhances the data classification capabilities of J48 but also increased the data space for the algorithm towards giving more accurate results. Methods/Analysis: The datasets related to heart and liver disease were selected from the UCI machine learning repositories. The experiment has been conducted with the help of WEKA tool, which is an open source tool for data mining. Finding: After experimentation it is found that the efficiency of J48 is giving better classification accuracy with reduced error rate when applied with datasets after inclusion of newly generated attributes by genetic programming. After adding attributes induced by genetic programming, significant efficiency boost can be seen in classification capabilities of J48 by 74% to 83% and 68% to 72% for heart and liver disease datasets respectively. Improvement: We obtained better results when compared to the existing literature for the chosen clinical datasets.

Sabita Khatri

Papers

Implementing WEKA for medical data classification and early disease prediction

Optimizing Decision Tree Through Attributes Generation Using Genetic Programming for Clinical Data