Age, gender, and residence differences in prevalence and patterns of multimorbidity among older Chinese: Evidence from Chinese Longitudinal Healthy Longevity Survey

Background With the dramatic acceleration of ageing in China, multimorbidity among the older adults has become increasingly common，which are associated with more functional decline and higher health care utilization and mortality. Understanding demographic differences of patterns of multimorbidity is in favor of making targeted intervention strategies. The purpose of this study was to reveal age- specific, gender- specific, and residence- specific prevalence and patterns of multimorbidity among older adults in China. Methods The present analysis is based on the 2018 wave of Chinese Longitudinal Health Longevity Survey (CLHLS). We selected 13 chronic diseases from the CLHLS survey, and information was collected based on self-report. Multimorbidity was defined as the coexistence of two or more chronic diseases from 13 chronic diseases in the same individual. Descriptive statistical analysis was used to examine multimorbidity according to age, sex, and residence. Patterns and trends of chronic disease pairs and multimorbidity were explored using association rule mining. Results 9,660 individuals aged 65-117 years in the CLHLS were analyzed in this study. Overall, 74.4% of all participants had one or more morbidities, and 42.4% were multimorbid. The prevalence of individual chronic diseases ranged from 1.5% for cancer to 41.8% for hypertension, and each disease was often accompanied by one or more other chronic diseases. The prevalence of multimorbidity does not always increase with age. The subgroups with the highest prevalence of multimorbidity was 80-89 years old (48.2%), female (45.0%) and urban (47.2%) group. Prevalence of the hypertension- diabetes pattern decreases with age and is higher in women than in men. The prevalence of hypertension- depression pattern was at the highest among the 90-117 years and rural older adults, while the other groups were hypertension-heart disease. Moreover, it was noteworthy that the multimorbidity rate of dyslipidemia is the highest at 95.5% among the 13 chronic diseases. Conclusions The prevalence of multimorbidity among older Chinese was substantial, and patterns of multimorbidity differed in age, sex, and residence. Future efforts are needed to identify possible prevention strategies and guidelines targeted demographic differences of multimorbid patients to promote health in older adults.

Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.

Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Analysis of high-dimensional data, with more features () than observations () (), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

Fuzzy association rule mining approach to identify e-commerce product association considering sales amount

Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers’ choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.