Data Mining Technique for Medical Diagnosis Using a New Smooth Support Vector Machine

In this paper, the application of data mining techniques on polynomial based approach for better electrocardiogram (ECG) authentication mechanism is presented. Polynomials being used for ECG data processing have a history of nearly two decades. Recently it has been bringing about promising solutions for heart beat recognition problem. General polynomial based approach are used in this research and by using the polynomial coefficients extracted as unique features from the ECG signals, data mining techniques was applied for person identification. A total of 18 ECG recordings from MIT/BIH Normal Sinus Rhythm database (NSRDB) were used for development and evaluation. QRS complexes from each dataset was divided into two parts, the training and the testing dataset which was used to prove the validity of the data mining technique applied. Experimental results was classified using Multilayer Perceptron (MLP) in order to confirm the identity of an individual and was compared with the previous research using polynomials without the use of data mining technique. Our experimentation on a public ECG database suggest that the proposed data mining technique on polynomial based approach significantly improves the identification accuracy by 96% as compared to 87% from the existing study.

A review on application of data mining techniques to combat natural disasters

The healthcare environment is generally perceived as being 'information rich' yet 'knowledge poor'. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, Decision tree, Naive Bayes and Artificial Neural Network to massive volume of healthcare data. The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not "mined" to discover hidden information. For data preprocessing and effective decision making One Dependency Augmented Naive Bayes classifier (ODANB) and naive credal classifier 2 (NCC2) are used. This is an extension of naive Bayes to imprecise probabilities that aims at delivering robust classifications also when dealing with small or incomplete data sets. Discovery of hidden patterns and relationships often goes unexploited. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established.

The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, decision tree and Artificial Neural Network to massive volume of healthcare data. In particular we consider a case study using classification techniques on a medical data set of diabetic patients.

Knowledge discovery in financial organization have been built and operated mainly to support decision making using knowledge as strategic factor. In this paper, we investigate the use of various data mining techniques for knowledge discovery in insurance business. Existing software are inefficient in showing such data characteristics. We introduce different exhibits for discovering knowledge in the form of association rules, clustering, classification and correlation suitable for data characteristics. Proposed data mining techniques, the decision- maker can define the expansion of insurance activities to empower the different forces in existing life insurance sector.

A disease is an abnormal condition that affects the structure and function of one or more parts of the body. It may be caused by various factors, external and internal dysfunctions. There is a trend of various chronic diseases in any society. The major concern is that these chronic diseases are leading to many other diseases in future. An attempt to explore the correlation of various chronic diseases has become a necessity. This can be achieved by using data mining techniques, which help to derive knowledge about the affects of a particular chronic disease on the other chronic diseases. Since there is growing trend of diabetes and ischemic heart disease in the society, in this paper the focus is to investigate the effect of these diseases on the other chronic diseases using the ICD9 diagnostic codes. To achieve this goal various types of data mining techniques are used. The conclusion is an optimal set of ICD9 diagnostic codes associated with individuals having diabetes or ischemic heart disease. These codes are then investigated based on the human anatomic systems i.e. Circulatory system, Respiratory system, Nervous system, Musculoskeletal system, Renal system and Neoplasm and their relevance is justified.

Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.

Smart farming is a development that highlights the use of technologies such as the internet of things, cloud computing, machine learning, and artificial intelligence in the farm management cycle. For sustainable agriculture to adapt the ongoing change in climate and social structure is a major challenge for scientists and researchers. The approach needs information from various sources and its use in the relevant field, which lead to a growing interest in knowledge discovery from large data. Data mining techniques provide effective solutions for this problem as it supports the automation of extracting significant data to obtain knowledge and trends, the elimination of manual tasks, easier data extraction directly from electronic sources, and transfer to secure electronic system of documentation, which will increase the agriculture productions from same limited resources. In a nutshell, the aim of this chapter is to gain insight into the applications of data mining techniques in smart farming, which direction to employ sustainable agriculture and identify the challenges to be addressed.

The research objective is to cluster the Unmet Need of health services in Indonesia by region by utilizing data mining techniques. The data source was obtained from the Central Statistics Agency (abbreviated BPS) in the health category using Unmet Need data on health services by province (2015-2018) consisting of 34 data records. The variable used is the Unmet Need of health services by province (percent). The data mining technique used is k-means which is part of clustering. Data processing is performed using RapidMiner 5.3 software. The cluster labels used in the study are high cluster (C1) and low cluster (C2). The results state that the application of k-means can be carried out where 9 provinces are in cluster C1 consisting of Riau, West Nusa Tenggara, East Nusa Tenggara, Central Kalimantan, South Borneo, Central Sulawesi, Southeast Sulawesi, Gorontalo and West Sulawesi with final centroids (C ) = 6.96 and 25 provinces are in cluster C2 with final centroid (C) = 4.36. The results of mapping in the form of regional clusters can provide information for the government and should Unmet Need health services in Indonesia must be worth 0% for each region.

Forest fire is one of the most complex phenomena which can cause great economic losses and make eco-environment seriously disordered. Forest fire has caused the loss of many green acres in Lebanon due to the lack of governmental policies in order to mange forest fires. This paper presents an overview of the exciting applications of data mining techniques in different fields. This study aims to predict forest fires in North Lebanon in order to reduce fire occurrence based on 4 meteorological parameters (Temperature, Humidity, Precipitation and Wind speed) using different data mining techniques: Neural networks, decision tree (J48), fuzzy logic, support vector machine (SVM) and linear discriminant analysis (LDA). A comparative study is then made to find the best performing technique tending to manage such a natural crisis. Decision tree (J48) recorded the best accuracy in forest fire prediction (97.8%).

The significance of this research lies in the application of data mining techniques to analyze badminton competitions, allowing for the identification of players’ tactics, strategies, and weaknesses. By uncovering these insights, targeted training and improvement plans can be developed, enhancing player performance and optimizing competitive outcomes. Association rules in data mining techniques are used, and the Apriori algorithm in association rules is analyzed and improved. The ACARMI algorithm (an improved algorithm of constrained association rule mining based on item) is then applied. Then, data from six top badminton competitions is collected and converted into Boolean data suitable for this algorithm. A data mining module is designed to mine the tactical association rules of all players, and a specific analysis is conducted on one of the players, successfully analyzing their tactical strategies. Finally, to verify the effectiveness of the algorithm proposed in this article, FP-Growth algorithm and DHP algorithm are applied to comparative experiments. The experiment shows that compared with the FP-Growth algorithm, the initial and final running times of the algorithm in this article with minimum support and different constraint numbers are around “355 ms and 75 ms,” “150 ms and 45 ms,” respectively; similarly, compared with the DHP algorithm, they are around “165 ms and 35 ms,” “175 ms and 55 ms,” respectively. Under two sets of experiments, the algorithm proposed in this article presents better operational efficiency. This article demonstrates that the use of data mining techniques can provide effective tactical optimization, opponent research, real-time decision support, and long-term development planning assistance for athletes and coaches.

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Introduction: Prostate cancer is one of the leading causes of death in men, and the early detection of this disease can be a significant factor in controlling and managing it. Applying data mining techniques can lead to the extraction of hidden knowledge from a huge amount of data and can help diagnose this disease by physicians. This study aims to determine the algorithm with the best performance to diagnose prostate cancer.Methods: In this study, nine data mining techniques, including Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, Neural Network, Random Forest, Deep Learning, Auto-MLP, and Rule Induction algorithms, were used to extract hidden patterns from prostate cancer data. In this study, the data of 100 patients, which included eight characteristics, were used, and the RapidMiner Studio environment was employed for modeling. To compare the performance of the mentioned approaches used in this study to diagnose prostate cancer, accuracy, recall, precision, AUC, sensitivity, and specificity were calculated and reported for all techniques.Results: The results of this study showed that the accuracy of the applied algorithms was between 77% and 84%. Using different criteria to evaluate the techniques used showed that the two algorithms K-Nearest Neighbors and Neural Network, had better performance and accuracy (84%) than other methods. The sensitivity in these two algorithms was 80% for Neural Networks and 85% for K-Nearest Neighbors, respectively.Conclusion: The usage of different data mining techniques can lead to the discovery of hidden patterns among an enormous amount of data related to prostate cancer, and as a result, it leads to the early diagnosis of this disease and saves the subsequent costs.

The decision making process in any field related to human behavior is an integral part of complex environment. Education has become a major concern of the development process in every field. The competent decision making to plan, execute and evaluate policies in this field became a necessity of this field which may be achieved by applying data mining techniques to educational environment. EDM uses many techniques such as decision trees, neural networks, k-nearest neighbor, naive bays, support vector machines and many others. The principal purpose of the study is identifying how each of these traditional processes can be improved through data mining techniques. And also to analyze the student behavior for future benefit by applying Data mining technique and using this valuable information for Decision Support to Prognostication of Academic Intervention for Higher Education using Data Mining Techniques. The study has suggested models and algorithms for every educational sphere we found while analyzing Education database. Data modeling processes is our main outcome, and enhanced processes achieved through data mining have been presented as research outcome. The techniques appropriate in achieving this enhanced processes has also been presented as modeling processes for finding the behavioral aspects and to effectively implement the organizational policies.

In this article, the Internet of Medical Things (IoMT) framework based on Apache Spark big data processing technology is proposed for real‐time analysis of health data obtained from wireless body area networks (WBANs), which is one of the most important components of IoMT. The proposed framework consists of four layers: data source, data collection, data analytics and visualization. In addition, the proposed IoMT framework is presented with two different disease prediction scenarios, diabetes and heart disease. Diabetes and heart disease prediction processes are carried out using the random forest (RF), logistic regression (LR) and support vector machine (SVM) algorithms belonging to the Apache Spark machine learning library (MLlib). The analysis of health data generated in WBANs takes place in real‐time in the Apache Spark‐based data analytics layer. In this study, the performances of MLlib algorithms in the real‐time model developed for heart and diabetes disease are examined. The SVM algorithm with an accuracy rate of 93.33% for heart disease and the LR algorithm with an accuracy rate of 78.89% for diabetes are found to provide the best performances.