Applying Data Mining in Smart Home
Bruno Bouchard in Smart Technologies in Healthcare, 2017
Data mining is the set of methods and algorithms allowing exploration and analysis of database (Ian H. Witten and Franck 2010). It exploits tools from statistics, artificial intelligence and database management system. Data mining is used to find patterns, association, rules or trends in datasets and usually to infer knowledge on the essential parts of the information (Quinlan and Ghosh 2006). It is often seen as a subtopic of machine learning. However, machine learning is typically supervised, since the goal is to simulate the learning of known properties from experience (training set) in an intelligent system. Therefore, a human expert usually guides the machine in the learning phase (Barlow 1989). Within realistic situations, it is often not the case. While the two are similar in many ways, generally, in data mining, the goal is to discover previously unknown knowledge (Chaudhuri et al. 2011) that can then be exploited in intelligent systems and business intelligence to arrive at better decisions.
Performance of Diverse Machine Learning Algorithms for Heart Disease Prognosis
Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood in Computational Intelligence and Data Sciences, 2022
Health researchers have produced a vast collection of medical evidence that can be analyzed, and useful information can be extracted from it. Data mining techniques are methods for retrieving useful information from vast amounts of data [9]. Large networks of data in a medical database are discrete [10]. As a consequence, making decisions based on discrete data becomes a daunting challenge. Machine learning (ML), a subfield of data mining, excels at handling massive, well-formatted, normalized datasets. ML is a tool that can be used to diagnose, track, and forecast different diseases in the medical field [11]. The goal is to make the process easier and to deliver successful care to patients while avoiding serious repercussions [12]. The role of ML in detecting hidden discrete patterns and analyzing the data is critical. Following data processing and dimensionality reduction, ML methods aid in the early detection and speedy diagnosis of heart disease. This chapter aims at testing the efficacy and the potential of numerous ML and deep learning [13] techniques for predicting cardiac disease at an early level (Figure 1.1).
Swarm Intelligence and Evolutionary Algorithms for Heart Disease Diagnosis
Sandeep Kumar, Anand Nayyar, Anand Paul in Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development, 2019
In literature, the commonly tools used for data mining in heart disease diagnosis are Weka [32,33], MATLAB [24], TANAGRA [34], Statistical Analysis System (SAS) software [11]. The data mining techniques are Naive Bayes, Neural Networks, Decision Trees [35], Fuzzy logic [36], and Multilayer Perceptrons [29]. Yang et al. [8] discussed the diagnosis of vavular malfunction-based heart disease using neural network techniques. The authors used SAS software for experimentation and achieved maximum accuracy as 97.4%. Also, they showed 89.01% accuracy for diagnosis of comprehensive heart diseases. Bhatla and Jyoti [34]discussed different data mining techniques for heart diseases diagnosis, namely Navie Bayes, Neural Network and Decision Tree. The various tools like Weka, net platform, TANGRA were used. It was noted that accuracy of 99% was achieved using Weka tool and Naive Bayes mechanism. Whereas minimum of 45% accuracy was achieved using decision tree under TANGRA software. Alizadehsami et al. [37] studied various data mining techniques like C4.5, multilayer perceptron, and neural network using Weka tool. Under this, the accuracy of 100% was achieved for prediction of heart disease as part of health care decision support system. Taneja [32] discussed J48 and Naive Bayes algorithms for Heart disease diagnosis using Weka tool. The authors has achieved maximum of 95.54% accuracy under various experimentation scenarios.
Advances with support vector machines for novel drug discovery
Published in Expert Opinion on Drug Discovery, 2019
Vinicius Gonçalves Maltarollo, Thales Kronenberger, Gabriel Zarzana Espinoza, Patricia Rufino Oliveira, Kathia Maria Honorio
Overall, data mining can be defined as the automatic extraction of useful information from large databases using algorithms in order to discover patterns and correlations within these data sets. It has been said that ‘More data has been created in the past two years than in the entire previous history of the human race’ [30], a process mostly driven by consumer-oriented data recording by companies, but with applications ranging over the most diverse fields. In the life sciences, the most benefited fields are cheminformatics, computational genomics and biomedical imaging [31]. Indeed, in the field of cheminformatics, the availability of new computational resources (primarily hardware, for example, use of graphical processing units – GPUs [32]) enabled the use of demanding algorithms, which can employ feed-forward networks such as deep learning (DL, as extensively reviewed in [33]) with multiple processing layers instead of the classical single-layer model. Cano demonstrated, using prediction of drug solubility as a model, that the use of architectures with GPU can accelerate SVM up to 15 times when compared to its sequential counterpart implementation version [34]. Despite the recent popularity of DL, classical ML techniques with a focus on SVM are still widely employed in drug discovery. Next, the main characteristics of SVM will be discussed along with exemplary medicinal chemistry studies that have applied this technique. Furthermore, some features of the SVM formulation will be presented.
A hybrid clustering and classification approach for predicting crash injury severity on rural roads
Published in International Journal of Injury Control and Safety Promotion, 2018
Seyed Hessam-Allah Hasheminejad, Mohsen Zahedi, Seyed Mohammad Hossein Hasheminejad
Data mining has been used in different fields such as diagnosis of heart diseases (Homayounfar, Sepehri, Hasheminejad, & Ghobakhloo, 2014), text-mining (Sebastiani, 2002), designing software architecture (Hasheminejad & Jalili, 2013), selecting design pattern (Hasheminejad & Jalili, 2009) and so on. Essentially, data mining discovers patterns and relationships hidden in your data (Edelstein, 1997). Data mining refers to all aspects of an automated or semi-automated process to extract unknown and useful knowledge and patterns from large databases. This process consists of two steps: the first step is data pre-processing that includes noise removal, feature selection and data conversion to usable format for data mining. In the second step, the data obtained from the first step is used in order to pattern recognition, which is done using algorithms such as classification and clustering. Therefore, the obtained patterns are evaluated based on a set of criteria such as Recall, Precision and Accuracy (Alpaydin, 2014).
Potential value and impact of data mining and machine learning in clinical diagnostics
Published in Critical Reviews in Clinical Laboratory Sciences, 2021
Maryam Saberi-Karimian, Zahra Khorasanchi, Hamideh Ghazizadeh, Maryam Tayefi, Sara Saffar, Gordon A. Ferns, Majid Ghayour-Mobarhan
Data mining is considered to be a process within the field of computer science [4]. Data mining tools differ from conventional predictive models. These models describe historical data, while data mining predicts future events. Despite using the same core methods to perform the analysis, the data mining process includes different pre-processing and post-processing steps [1]. The pre-processing steps include data cleaning, and this is undertaken before applying the mining algorithms. The post-processing steps are applied to better visualize the results obtained from the analysis in a more intelligible way [1]. Data mining involves a combination of statistics, artificial intelligence, machine learning, and mathematical sciences, including database technology, pattern recognition, information retrieval, neural networks, knowledge-based systems, high-performance computing, and data visualization (Figure 1) [5]. Artificial intelligence methods (e.g. machine learning) and visualization techniques [4] have become increasingly popular.
Related Knowledge Centers
- Anomaly Detection
- Artificial Intelligence
- Cluster Analysis
- Neural Network
- Statistical Inference
- Information Processing
- Data Collection
- Association Rule Learning
- Sequential Pattern Mining
- Data Dredging