scholarly journals Disease Diagnosis System using Machine Learning

2021 ◽  
pp. 185-194
Author(s):  
Shailesh D. Kamble ◽  
Pawan Patel ◽  
Punit Fulzele ◽  
Yash Bangde ◽  
Hitesh Musale ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Disease Diagnosis ◽  
Current Data ◽  
The Body ◽  
Training Set ◽  
Use Of Data ◽  
Common Illnesses ◽  
Heath Care ◽  
Professional Model

The efficient use of data mining in virtual sectors such as e-соmmerсe, and соmmerсe has led to its use in other industries. The mediсаl environment is still rich but weaker in technical analysis field. There is а lot of information that саn оссur within mediсаl systems. Using powerful analytics tооls to identify the hidden relationships with the current data trends. Disease is а term that provides а large number of соnditiоns connected to the heath care. These mediсаl соnditiоns describe unexpected health соnditiоns that directly соntrоl all the оrgаns of the body. Mediсаl data mining methods such as соrроrаte management mines, сlаssifiсаtiоn, integration is used to аnаlyze various types of соmmоn рhysiсаl problems. Seраrаtiоn is an imроrtаnt рrоblem in data mining. Many рорulаr сliрs make decision trees to рrоduсe саtegоry models. Data сlаssifiсаtiоn is based on the ID3 decision tree algorithm that leads to ассurасy, data are estimated to use entrорy verifiсаtiоn methods based on сrоss-seсtiоnаl and segmentation and results are соmраred. The database used for mасhine learning is divided into 3 parts - training, testing, and finally validation. This approach uses а training set to train а model and define its аррrорriаte раrаmeters. А test set is required to test а professional model and its standard performance. It is estimated that 70% of people in India can catch common illnesses such as viruses, flu, coughs, colds etc. every two months. Because most people do not realize that common allergies can be symptoms of something very serious, 25% of people suddenly die from ignoring the first normal symptoms. Therefore, identifying or predicting the disease early using machine learning (ML) is very important to avoid any unwanted injuries.

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

scholarly journals Use of data mining at the Food and Drug Administration

2015 ◽  
Vol 23 (2) ◽  
pp. 428-434 ◽  
Author(s):  
Hesha J Duggirala ◽  
Joseph M Tonning ◽  
Ella Smith ◽  
Roselie A Bright ◽  
John D Baker ◽  
...  
Keyword(s):  
Data Mining ◽  
Drug Administration ◽  
Food And Drug Administration ◽  
The United States ◽  
Current Data ◽  
Mining Activities ◽  
Use Of Data ◽  
Address Data ◽  
United States Food ◽  
Veterinary Products

Abstract Objectives This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA). Target audience We address data miners in all sectors, anyone interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities. Scope Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA.

scholarly journals A Review on Various Algorithms used in Machine Learning

2019 ◽  
pp. 915-920
Author(s):  
Divya Chaudhary ◽  
Er. Richa Vasuja
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
New Technologies ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Training Set ◽  
Learning Data ◽  
Do So

In today's scenario all of data is being generated by everyone of us . so it becomes vital for us to handle this data. To do so new technologies are being developed such as machine learning, data mining etc. This paper gives the study related to machine learning(ML).Precise approximations are repetitively being produced by Machine Learning algorithms. Machine learning system effectively “learns” how to guess from training set of completed jobs. The main purpose of the review is to give a jagged estimate or overview about the mostly used algorithms in machine learning.

scholarly journals Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection

2020 ◽  
Vol 9 (3) ◽  
pp. 4084-4090
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cyber Security ◽  
Data Stream ◽  
Machine Learning Techniques ◽  
Complex Nature ◽  
Use Of Data ◽  
Learning Techniques ◽  
Examination Paper ◽  
Discovery Rates

An interference discovery framework is customizing that screens a singular or an arrangement of PCs for toxic activities that are away for taking or blue-penciling information or spoiling framework shows. The most methodology used as a piece of the present interference recognition framework is not prepared to deal with the dynamic and complex nature of computerized attacks on PC frameworks. In spite of the way that compelling adaptable methodologies like various frameworks of AI can realize higher discovery rates, cut down bogus alert rates and reasonable estimation and correspondence cost. The use of data mining can realize ceaseless model mining, request, gathering and littler than ordinary data stream. This examination paper portrays a connected with composing audit of AI and data delving procedures for advanced examination in the assistance of interference discovery. In perspective on the number of references or the congruity of a rising methodology, papers addressing each procedure were recognized, examined, and compacted. Since data is so fundamental in AI and data mining draws near, some striking advanced educational records used as a piece of AI and data burrowing are depicted for computerized security is shown, and a couple of recommendations on when to use a given system are given.

scholarly journals Application of Data Mining in the Guidance of Sports Training

2013 ◽  
Vol 765-767 ◽  
pp. 1518-1523
Author(s):  
Fan Hui Meng ◽  
Qing Li Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Experimental Data ◽  
Data Mining ◽  
Statistical Learning ◽  
Athletic Performance ◽  
Sports Training ◽  
Mining Technology ◽  
Broad Application ◽  
Use Of Data

Data mining is the techniques of finding the potential law from the data by machine learning and statistical learning .This paper focuses on a number of problems existed in the currents ports training, discusses the application principle of the data mining technology in sports training, and applies the critical neural networks for forecasting the performances of the athletes .Experimental data show that prediction of athletic performance by the use of neural network has very good approximation ability. It shows a broad application space of the use of data mining technology.

scholarly journals New method for rapid diagnosis of Hepatitis disease based on reduction feature and machine learning

2015 ◽  
Vol 4 (1) ◽  
pp. 148
Author(s):  
Nahid Khorashadizade ◽  
Hassan Rezaei
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Classification Accuracy ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Rapid Diagnosis ◽  
Machine Learning Algorithms ◽  
University Of California ◽  
Learning Machine ◽  
Two Stages

<p>Hepatitis disease is caused by liver injury. Rapid diagnosis of this disease prevents its development and suffering to cirrhosis of the liver. Data mining is a new branch of science that helps physicians for proper decision making. In data mining using reduction feature and machine learning algorithms are useful for reducing the complexity of the problem and method of disease diagnosis, respectively. In this study, a new algorithm is proposed for hepatitis diagnosis according to Principal Component Analysis (PCA) and Error Minimized Extreme Learning Machine (EMELM). The algorithm includes two stages; in reduction feature phase, missing records were deleted and hepatitis dataset was normalized in [0,1] range. Thereafter, analysis of the principal component was applied for reduction feature. In classification phase, the reduced dataset is classified using EMELM. For evaluation of the algorithm, hepatitis disease dataset from UCI Machine Learning Repository (University of California) was selected. The features of this dataset reduced from 19 to 6 using PCA and the accuracy of the reduced dataset was obtained using EMELM. The results revealed that the proposed hybrid intelligent diagnosis system reached the higher classification accuracy and shorter time compared with other methods.<strong></strong></p>

scholarly journals Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

10.2196/20921 ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. e20921
Author(s):  
Qiang Pan ◽  
Damien Brulin ◽  
Eric Campo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Monitoring System ◽  
The Body ◽  
Monitoring Systems ◽  
Sleep Monitoring ◽  
Advantages And Disadvantages ◽  
System Solution

Background Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. Objective This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. Results By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Conclusions Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

scholarly journals Machine Learning and data mining tools applied for databases of low number of records

2022 ◽  
Vol 21 (4) ◽  
pp. 346-363
Author(s):  
Hubert Anysz
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computational Methods ◽  
Large Datasets ◽  
Learning Tools ◽  
Data Preparation ◽  
Preparation Methods ◽  
Use Of Data ◽  
Small Set ◽  
Mining Tools

The use of data mining and machine learning tools is becoming increasingly common. Their usefulness is mainly noticeable in the case of large datasets, when information to be found or new relationships are extracted from information noise. The development of these tools means that datasets with much fewer records are being explored, usually associated with specific phenomena. This specificity most often causes the impossibility of increasing the number of cases, and that can facilitate the search for dependences in the phenomena under study. The paper discusses the features of applying the selected tools to a small set of data. Attempts have been made to present methods of data preparation, methods for calculating the performance of tools, taking into account the specifics of databases with a small number of records. The techniques selected by the author are proposed, which helped to break the deadlock in calculations, i.e., to get results much worse than expected. The need to apply methods to improve the accuracy of forecasts and the accuracy of classification was caused by a small amount of analysed data. This paper is not a review of popular methods of machine learning and data mining; nevertheless, the collected and presented material will help the reader to shorten the path to obtaining satisfactory results when using the described computational methods

Using data mining to process business data

2020 ◽  
pp. 71-77
Author(s):  
Chubukova ◽  
Ponomarenko ◽  
Nedbailo
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Programming Languages ◽  
Complex Analysis ◽  
Primary Data ◽  
Business Analytics ◽  
Use Of Data ◽  
The Subject ◽  
Mining Methods ◽  
Using Data

The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); monograph (studying the preconditions for the growth of data mining companies’ use in the process of data research); eco- nomic analysis (when assessing the feasibility of using machine learning methods to ensure the goals of business intelligence). The scientific novelty consists the peculiarities of data mining application as one of the directions of business analyt- ics are determined, which makes it possible to determine implicit relationships between known factor and result characteristics on the basis of primary data. The main directions of data manipulation are revealed: classification and forecasting, as well as correlation-regression analysis. The importance of using the basic meth- ods of statistical analysis in the process of studying primary data is proved. The specifics of the use of neural networks as one of the most important methods of machine learning are given. Conclusions. The use of data mining allows companies to increase the efficiency of the use of available data and optimize development strategies accordingly. The presence of a large number of machine learning meth- ods and statistical approaches expands the possibilities of comprehensive data analysis. Innovative technologies and specialized programming languages play an important role in this case.

scholarly journals Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors

2015 ◽  
Vol 6 ◽  
pp. 1886-1896 ◽  
Author(s):  
David E Jones ◽  
Hamidreza Ghandehari ◽  
Julio C Facelli
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Predictive Models ◽  
Molecular Descriptors ◽  
Pamam Dendrimer ◽  
Pamam Dendrimers ◽  
Journal Articles ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Cancer Nanomedicine

The use of data mining techniques in the field of nanomedicine has been very limited. In this paper we demonstrate that data mining techniques can be used for the development of predictive models of the cytotoxicity of poly(amido amine) (PAMAM) dendrimers using their chemical and structural properties. We present predictive models developed using 103 PAMAM dendrimer cytotoxicity values that were extracted from twelve cancer nanomedicine journal articles. The results indicate that data mining and machine learning can be effectively used to predict the cytotoxicity of PAMAM dendrimers on Caco-2 cells.

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