Blood tumor prediction using data mining techniques

Healthcare systems generate a huge data collected from medical tests. Data mining is the computing process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an exception; there are many test data can be collected from their patients. In this paper, we applied data mining techniques to discover the relations between blood test characteristics and blood tumor in order to predict the disease in an early stage, which can be used to enhance the curing ability. We conducted experiments in our blood test dataset using three different data mining techniques which are association rules, rule induction and deep learning. The goal of our experiments is to generate models that can distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our results using different metrics applied on real data collected from Gaza European hospital in Palestine. The final results showed that association rules could give us the relationship between blood test characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an explanation of rules that describes both tumor in blood and normal hematology

Mining Implicit Patterns of Customer Purchasing Behavior Based On The Consideration Of RFM Model

Association rules have been developed for years and applied successfully for market basket analysis and cross selling among other business applications. One of the most used approaches in association rules is the Apriori algorithm. However the Apriori algorithm, has long known for its weaknesses that generate enormous amount of rules and alreadyknown facts. In this study, we integrate the RFM attributes with the classical association rule mining, Apriori. Based on RFM model, two indicators, RF score and Sale ratio, are used as measure of interestingness. We propose two algorithms, DWRF and DWRFE, to mine for implicit pattern. In our experimental evaluation, the performance of Apriori, DWRF and DWRFE are compared. The result of our algorithms offers an effective measurement of interesting patterns. Moreover, the DWRF algorithm that uses the RF score as a measure of interestingness seems to be able to promptly reflect the fast-changing customer’s purchase patterns

A new strategy for case-based reasoning retrieval using classification based on association

This paper proposes a novel strategy, Case-Based Reasoning Using Association Rules (CBRAR) to improve the performance of the Similarity base Retrieval SBR, classed frequent pattern trees FP-CAR algorithm, in order to disambiguate wrongly retrieved cases in Case-Based Reasoning (CBR). CBRAR use class as-sociation rules (CARs) to generate an optimum FP-tree which holds a value of each node. The possible advantage offered is that more efficient results can be gained when SBR returns uncertain answers. We compare the CBR Query as a pattern with FP-CAR patterns to identify the longest length of the voted class. If the patterns are matched, the proposed strategy can select not just the most similar case but the correct one. Our experimental evaluation on real data from the UCI repository indicates that the proposed CBRAR is a better approach when com-pared to the accuracy of the CBR systems used in our experiments

Providing a Service for Interactive Online Decision Aids through Estimating Consumers\u27 Incremental Search Benefits

Consumer information search has been a focus of research nowadays, especially in the context of online business environments. One of the research questions is to determine how much information to search (i.e., when to stop searching), since extensive literature on behavior science has revealed that consumers often search either “too little” or “too much”, even with the help of existing interactive online decision aids (IODAs). In order to address this issue, this paper introduces a new approach to IODAs with effective estimation of the incremental search benefits. In doing so, the approach incorporates two important aspects into consideration, namely point estimation and distribution estimation, so as to make use of the relevant information by combining both current and historical facts in reflecting the behavioral patterns of the consumers in search. Moreover, experiments based on data provided by Netflix illustrate that the proposed approach is effective and advantageous over existing ones

Trend Analysis of Physical Activity Measurement Research Using Text Mining in Big Data Analytics

Measurements of physical activity taken in a valid and reliable way are essential in characterizing the relationship between physical activity and health outcomes. Given the steadily growing interest in the physical activity measurement and the lack of research to identify current trends, this study investigated the research trend of physical activity measurement by applying four text data mining techniques (i.e., future signal, keyword network analysis, keyword trend, and keyword association rule). A total of 54,670 publications from 1982 to 2021 were collected from PubMed. As a result, the current study 1) confirmed two weak signal topics (i.e., “validity of physical activity instrument” and “classification of physical activity patterns using machine learning algorithms”) that are likely to affect future research trends, 2) identified keywords (e.g., “youth,” “adult,” “woman,” “survey,” “questionnaire,” and “monitor”) from the perspective of populations and measurement tools, 3) examined that the relative importance of keyword, “senior” increased rapidly, and 4) indicated that new keywords (i.e., “smartphone,” “wearable device,” “GPS,” “tracker,” and “app”) appeared in the early 2000s. The findings of this study provided implications for the selection of research topics and the use of text mining techniques in physical activity measurement research