A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Analyzing Lifestyle and Environmental Factors on Semen Fertility using Association Rule Mining

The data mining has been used to extract hidden knowledge more effectively for analysis of business, academic, agricultural, as well as medical data in contrast to the predefined queries or reports. This paper presents the impacts of lifestyle and environmental factors of a man on the fertility and quality of semen using association rule mining. The association rules have been mined from data collected by a normalized questionnaire from young volunteers and are found to be useful in predicting the quality of semen based on individual’s lifestyle and environmental factors. Keywords: Association rules, Knowledge Discovery, Fertility potential, Rule confidenc

Exploratory Analysis of Human Sleep Data

In this thesis we develop data mining techniques to analyze sleep irregularities in humans. We investigate the effects of several demographic, behavioral and emotional factors on sleep progression and on patient\u27s susceptibility to sleep-related and other disorders. Mining is performed over subjective and objective data collected from patients visiting the UMass Medical Center and the Day Kimball Hospital for treatment. Subjective data are obtained from patient responses to questions posed in a sleep questionnaire. Objective data comprise observations and clinical measurements recorded by sleep technicians using a suite of instruments together called polysomnogram. We create suitable filters to capture significant events within sleep epochs. We propose and employ a Window-based Association Rule Mining Algorithm to discover associations among sleep progression, pathology, demographics and other factors. This algorithm is a modified and extended version of the Set-and-Sequences Association Rule Mining Algorithm developed at WPI to support the mining of association rules from complex data types. We analyze both the medical as well as the statistical significance of the associations discovered by our algorithm. We also develop predictive classification models using logistic regression and compare the results with those obtained through association rule mining

A Novel Approach for Finding Rare Items Based on Multiple Minimum Support Framework

AbstractPattern mining methods describe valuable and advantageous items from a large amount of records stored in the corporate datasets and repositories. While mining, literature has almost singularly focused on frequent itemset but in many applications rare ones are of higher interest. For Example medical dataset can be considered, where rare combination of prodrome plays a vital role for the physicians. As rare items contain worthwhile information, researchers are making efforts to examine effective methodologies to extract the same. In this paper, an effort is made to analyze the complete set of rare items for finding almost all possible rare association rules from the dataset. The Proposed approach makes use of Maximum constraint model for extracting the rare items. A new approach is efficient to mine rare association rules which can be defined as rules containing the rare items. Based on the study of relevant data structures of the mining space, this approach utilizes a tree structure to ascertain the rare items. Finally, it is demonstrated that this new approach is more virtuous and robust than the existing algorithms

Extracting Interval Temporal Logic Rules: A First Approach

Discovering association rules is a classical data mining task with a wide range of applications that include the medical, the financial, and the planning domains, among others. Modern rule extraction algorithms focus on static rules, typically expressed in the language of Horn propositional logic, as opposed to temporal ones, which have received less attention in the literature. Since in many application domains temporal information is stored in form of intervals, extracting interval-based temporal rules seems the natural choice. In this paper we extend the well-known algorithm APRIORI for rule extraction to discover interval temporal rules written in the Horn fragment of Halpern and Shoham\u27s interval temporal logic

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data

AbstractMicroarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient’s samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation

Semi-supervised incremental learning with few examples for discovering medical association rules

Background: Association Rules are one of the main ways to represent structural patterns underlying raw data. They represent dependencies between sets of observations contained in the data. The associations established by these rules are very useful in the medical domain, for example in the predictive health field. Classic algorithms for association rule mining give rise to huge amounts of possible rules that should be filtered in order to select those most likely to be true. Most of the proposed techniques for these tasks are unsupervised. However, the accuracy provided by unsupervised systems is limited. Conversely, resorting to annotated data for training supervised systems is expensive and time-consuming. The purpose of this research is to design a new semi-supervised algorithm that performs like supervised algorithms but uses an affordable amount of training data. Methods: In this work we propose a new semi-supervised data mining model that combines unsupervised techniques (Fisher's exact test) with limited supervision. Starting with a small seed of annotated data, the model improves results (F-measure) obtained, using a fully supervised system (standard supervised ML algorithms). The idea is based on utilising the agreement between the predictions of the supervised system and those of the unsupervised techniques in a series of iterative steps. Results: The new semi-supervised ML algorithm improves the results of supervised algorithms computed using the F-measure in the task of mining medical association rules, but training with an affordable amount of manually annotated data. Conclusions: Using a small amount of annotated data (which is easily achievable) leads to results similar to those of a supervised system. The proposal may be an important step for the practical development of techniques for mining association rules and generating new valuable scientific medical knowledge.This work has been partially supported by projects DOTT-HEALTH (PID2019-106942RB-C32, MCI/AEI/FEDER, UE). (Design of the study. Analysis and interpretation of data) and EXTRAE II (IMIENS 2019). (Design of the study. Analysis and interpretation of data. HUF corpus manual tagging. Writing of the manuscript), PI18CIII/00004 “Infobanco para uso secundario de datos basado en estándares de tecnología y conocimiento: implementación y evaluación de un infobanco de salud para CoRIS (Info-bank for the secondary use of data based on technology and knowledge standards: implementation and evaluation of a health info-bank for CoRIS) – SmartPITeS” (Data collection and HUF corpus construction), and PI18CIII/00019 - PI18/00890 - PI18/00981 “Arquitectura normalizada de datos clínicos para la generación de infobancos y su uso secundario en investigación: solución tecnológica (Clinical data normalized architecture for the genaration of info-banks and their secondary use in research: technological solution) – CAMAMA 4” (Data collection and HUF corpus construction) from Fondo de Investigación Sanitaria (FIS) Plan Nacional de I+D+i.S

Enhancing association rules algorithms for mining distributed databases. Integration of fast BitTable and multi-agent association rules mining in distributed medical databases for decision support.

Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients¿ records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients¿ personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making

MeTA: Characterization of medical treatments at different abstraction levels

Physicians and healthcare organizations always collect large amounts of data during patient care. These large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, the analysis of these datasets to gure out interesting and hidden knowledge is a challenging task. This paper proposes a new data mining framework based on generalized association rules to discover multiple-level correlations among patient data. Specically, correlations among prescribed examinations, drugs, and patient proles are discovered and analyzed at different abstraction levels. The rule extraction process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories. To ease the manual inspection of the result, a worthwhile subset of rules, i.e., the non-redundant generalized rules, is considered. Furthermore, rules are classied according to the involved data features (medical treatments or patient proles) and then explored in a top-down fashion, i.e., from the small subset of high-level rules a drill-down is performed to target more specic rules. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting rule groups at different abstraction levels