Data Mining - Concepts and Techniques.

Diabetes Prediction using Machine Learning Algorithms

https://cyberleninka.org/article/n/311474.pdf

Predictive Methodology for Diabetic Data Analysis in Big Data

The diabetes mellitus disease (DMD) commonly referred as diabetes is a significant public health problem. Predicting the disease at the early stage can save the valuable human resource. Voluminous datasets are available in various medical data repositories in the form of clinical patient records and pathological test reports which can be used for real-world applications to disclose the hidden knowledge. Various data mining (DM) methods can be applied to these datasets, stored in data warehouses for predicting DMD. The aim of this research is to predict diabetes based on some of the DM techniques like classification and clustering. Out of which, classification is one of the most suitable methods for predicting diabetes. In this study, J48 and Naive Bayesian techniques are used for the early detection of diabetes. This research will help to propose a quicker and more efficient technique for diagnosis of disease, leading to timely and proper treatment of patients. We have also proposed a model and elaborated it step-by-step, in order to make medical practitioner to explore and to understand the discovered rules better. The study also shows the algorithm generated on the dataset collected from college medical hospital as well as from online repository. In the end, an article also outlines how an intelligent diagnostic system works. A clinical trial of this proposed method involves local patients, which is still continuing and requires longer research and experimentation.

Classification of Diabetes Mellitus Disease (DMD): A Data Mining (DM) Approach

Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and Bio Stat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, a CART Ensembles

Medical professionals need a reliable prediction methodology to diagnose Diabetes. Data mining is the process of analysing data from different perspectives and summarizing it into useful information. The main goal of data mining is to discover new patterns for the users and to interpret the data patterns to provide meaningful and useful information for the users. Data mining is applied to find useful patterns to help in the important tasks of medical diagnosis and treatment. This project aims for mining the relationship in Diabetes data for efficient classification. The data mining methods and techniques will be explored to identify the suitable methods and techniques for efficient classification of Diabetes dataset and in mining useful patterns.

/pdf/application-of-data-mining-methods-and-techniques-for-2e260e24fc.pdf

Application of Data Mining Methods and Techniques for Diabetes Diagnosis

Web content mining retrieves the information from web in more structured forms. The page rank plays an essential part in web content mining process. Whenever user searches for any information on web, the relevant information is shown at top of list through page ranking. Many existing page ranking algorithms were developed and failed to rank the web pages in accurate manner through minimum time feeding. In direction to address the above mentioned issues, Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering (LSSPFS-SXGBC) Approach is introduced for page ranking based on user query. LSSPFS-SXGBC Approach has three processes for performing efficient web page ranking, namely preprocessing, feature selection and clustering. LSSPFS-SXGBC Approach in account of the numeral of operator request by way of an input. Lancaster Stemming Preprocessed Analysis is carried out in LSSPFS-SXGBC Approach for removing the noisy data from the input query. It eradicates the stem words, stop words and incomplete data for minimizing the time and space consumption. Sammon Projective Feature Selection Process is carried out in LSSPFS-SXGBC Approach to select the relevant features (i.e., keywords) based on user needs for efficient page ranking. Sammon Projection maps the high-dimensional space to lower dimensionality space to preserve the inter-point distance structure. After feature selection, Stochastic eXtreme Gradient Boost Page Rank Clustering process is carried out to cluster the similar keyword web pages based on their rank. Gradient Boost Page Rank Cluster is an ensemble of several weak clusters (i.e., X-means cluster). X-means cluster partitions the web pages into ‘x’ numeral of clusters where each reflection goes towards the cluster through adjacent mean value. For every weak cluster, selected features are considered as the training samples. Subsequently, all weak clusters are joined to form the strong cluster for attaining the webpage ranking results. By this way, an efficient page ranking is carried out through higher accurateness and minimum time consumption. The practical validation is carried out in LSSPFS-SXGBC Approach on factors such ranking accurateness, false positive rate, ranking time and space complexity with respect to numeral of user query.

/pdf/lancaster-stem-sammon-projective-feature-selection-based-57er9hch.pdf

Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering for Web Page Ranking

Transportation problem has been employed in different range of operations amd the optimization of such problem is significant in many areas. A simple penalty and rapid process is used in this paper in order to obtain the lowest shipping cost for the transportation problems. A new method is proposed to extract the optimal solution of transportation problem. First, we solve a transportation problem employing a new way, and we compare this new proposed method solution to the VAM and MODI methods. Following that, we obtain that the transportation solution of the proposed method is equal to the MODI solution. This result demonstrates that by utilizing the novel proposed strategy, we can identify the straight optimal solution to the majority of transportation problems. Finally, the solution process is mathematically presented.

A simple concept with minimum steps for solving the transportation problem to obtain the lowest shipping cost

: Software testing is the significant part of the software development process to guarantee software quality with testing a program for discovering the software bugs. But, the software testing has a long execution time by using huge number of test suites in the software development process. In order to overcome the issue, a novel technique called Hybridized Buffalo and Truncation Cyclic Gene Optimization-based Densely Connected Deep Neural Network (HBTCGO-DCDNN) introduced to improve the software testing accuracy with minimal time consumption. At first, the numbers of test cases are given to the input layer of the deep neural network layer. In the first hidden layer, the test suite generation process is carried out by applying the improved buffalo optimization technique with different objective functions namely time and cost. The improved buffalo optimization selects optimal test cases and generates the test suites. After the generation, the redundant test cases from the test suite are eliminated in the reduction process in the second hidden layer. The Truncative Cyclic Uniformed Gene Optimization technique is applied for the test suite reduction process based on thefault coverage rate. Finally, the reduced test suites are obtained at the output layer of the deep neural network The experimental evaluation of the HBTCGO-DCDNN and existing methods are discussed using the test suite generation time, test suite reduction rate as well as fault coverage rate. The comparative results of proposed HBTCGO-DCDNN technique provide lesser the generation time by 48% and higher test suit reduction rate by 19% as well as fault coverage rate 18% than the other well-known methods. HBTCGO-DCDNN that preserve their capability in detecting faults as compared with other related approaches. Among these approaches, it seems that HBTCGO-DCDNN uses the truncative cyclic uniformed gene optimization for finding the optimal test suites to test the software programs taken from the web chess dataset. The gene optimization finds the optimal test suites based on the fault coverage rates. The maximum fault coverage rates of the test suites are selected as optimal for testing the given software programs. The observed results suggest that HBTCGO-DCDNN achieves better results than the existing approaches.

/pdf/hybridization-of-buffalo-and-truncative-cyclic-gene-deep-1lap0imd.pdf

Hybridization of Buffalo and Truncative Cyclic Gene Deep Neural Network-based Test Suite Optimization for Software Testing

Increasing carbon emissions, and thus footprint, is one of the main reasons for the imbalance in environmental sustainability, which is primarily contributed to transportation. Transportation is a core functionality of logistics distribution and supply chain. In this paper, a hybrid gain-ant colony optimization and fruit fly optimization algorithm for green vehicle routing problem is proposed to plan shortest paths with reduced total fuel consumption efficiently. The proposed algorithm was simulated using the Erdogan and Miller Hooks dataset and compared with best-known solutions and existing methods.

V. Sangeetha

Papers

Application of Data Mining Methods and Techniques for Diabetes Diagnosis

Lancaster Stem Sammon Projective Feature Selection based Stochastic eXtreme Gradient Boost Clustering for Web Page Ranking

A simple concept with minimum steps for solving the transportation problem to obtain the lowest shipping cost

Hybridization of Buffalo and Truncative Cyclic Gene Deep Neural Network-based Test Suite Optimization for Software Testing

A Hybrid Gain-Ant Colony Algorithm for Green Vehicle Routing Problem