Drug Prediction System Using Data Mining Techniques - A Survey

2018 ◽  
pp. 32-43
Author(s):  
Jagadeesan V. ◽  
Dr. Palanivel K
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Learning Strategies ◽  
Clinical Decision Making ◽  
Machine Learning Algorithms ◽  
Knowledge Discovery In Databases ◽  
Treatment Quality ◽  
Mining Equipment ◽  
Data Mining Techniques ◽  
Using Data

The thriving Medical applications of Data mining in the fields of Medicine and Public health has led to the popularity of its use in Knowledge Discovery in Databases (KDD). Data mining has revealed novel Biomedical and Healthcare acquaintances for Clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Drug Prediction is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of Knowledge discovery in Databases using Data mining techniques that are in use in today’s Medical research. To enable the drug retrieval and the breakthrough of hidden retrieval patterns from related databases, a study is made. Also, the use of data mining to discover such relationships as those between Supervised and Unsupervised are presented. This paper summarizes various Machine learning algorithms based on various Data mining techniques in learning strategies. It has also been targeted on contemporary research being done the usage of the Data mining strategies to beautify the retrieval manner. This research paper offers destiny developments of modern-day strategies of KDD, using data mining equipment for medicinal drug industry. It also confers huge troubles and demanding situations related to information mining and medication area. The research discovered a developing quantity of records mining packages, such as evaluation of drugs names for higher fitness policy-making, detection of accurate effects with outbreaks and preventable from misclassified drug names.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

scholarly journals An Integrated  Approach for Cancer Survival Prediction Using Data Mining Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ishleen Kaur ◽  
M. N. Doja ◽  
Tanvir Ahmad ◽  
Musheer Ahmad ◽  
Amir Hussain ◽  
...  
Keyword(s):  
Data Mining ◽  
Ovarian Cancer ◽  
Cancer Patients ◽  
Missing Values ◽  
Life Quality ◽  
Advanced Ovarian Cancer ◽  
Machine Learning Algorithms ◽  
Survival Prediction ◽  
Data Mining Techniques ◽  
Using Data

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients’ survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

Data Mining Techniques in Agricultural and Environmental Sciences

2012 ◽  
pp. 311-325 ◽  
Author(s):  
Altannar Chinchuluun ◽  
Petros Xanthopoulos ◽  
Vera Tomaino ◽  
P.M. Pardalos
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Future Directions ◽  
Data Mining Techniques ◽  
Useful Knowledge ◽  
Future Developments ◽  
Large Sets ◽  
Agriculture And Environment ◽  
Advanced Knowledge ◽  
Using Data

Data mining techniques are largely used in different sectors of the economy and they increasingly are playing an important role in agriculture and environment-related areas. This paper aims to show our vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment. Efforts for searching hidden patterns in data are not a recent phenomenon. History shows that extensive observations on data have helped discover empirical laws in different fields of research. Therefore, it is important to provide researchers in agriculture and environmental-related areas with the most advanced knowledge discovery techniques. Data mining is the process of extracting important and useful information from large sets of data. This information can be converted into useful knowledge that could help to better understand the problem in study and to better predict future developments. The paper presents the state of the art in data mining and knowledge discovery techniques and provides discussions for future directions.

Exploration of Healthcare Using Data Mining Techniques

2018 ◽  
pp. 243-259 ◽  
Author(s):  
Anindita Desarkar ◽  
Ajanta Das
Keyword(s):  
Data Mining ◽  
Patient Care ◽  
Knowledge Discovery ◽  
Healthcare Cost ◽  
Healthcare Sector ◽  
Huge Amount ◽  
Data Set ◽  
Data Mining Techniques ◽  
Using Data ◽  
Tools And Techniques

Huge amount of data is generated from Healthcare transactions where data are complex, voluminous and heterogeneous in nature. This large dataset can be used as an ideal store which can be analyzed for knowledge discovery as well as various future predictions. So, Data mining is becoming increasingly popular as it offers set of innovative tools and techniques to handle this kind of data set whereas traditional methods have limitations for that. In summary, providing the better patient care and reduction in healthcare cost are two major goals of application of data mining in healthcare. Initially, this chapter explores on the various types of eHealth data and its characteristics. Subsequently it explores various domains in healthcare sector and shows how data mining plays a major role in those domains. Finally, it describes few common data mining techniques and their applications in eHealth domain.

scholarly journals Approach to Integration of Data Mining Techniques in Simulation Results Analysis

2018 ◽  
Vol 21 ◽  
pp. 86-92
Author(s):  
Irīna Šitova ◽  
Jelena Pečerska
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Theoretical Study ◽  
Queueing System ◽  
Decision Rules ◽  
Data Mining Techniques ◽  
Simulation Results ◽  
Using Data ◽  
Two Stages

The research is carried out in the area of analysis of simulation results by using data mining techniques. The goal of the research is to explore the applicability of data mining techniques in the area of simulation results analysis, to offer an application scheme of data mining techniques in the analysis of simulation results, as well as to demonstrate the usage of these techniques in the analysis of experimental data. As a result of the theoretical study, an approach is proposed, consisting of two stages and combining the fundamental techniques of data farming and knowledge discovery. A variety of data mining techniques, such as correlation analysis, clustering and several visualization mechanisms of results, are used for knowledge discovery. The proposed approach is applied to the analysis of experimental data. The performance of a queueing system is analysed, and knowledge and decision rules are obtained from simulation results.

scholarly journals Movie Success Prediction Using Data Mining

2021 ◽  
Vol 4 (2) ◽  
pp. 22-30
Author(s):  
Olubukola D.A. ◽  
Stephen O.M. ◽  
Funmilayo A.K. ◽  
Ayokunle O. ◽  
Oyebola A. ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Success Prediction ◽  
Movie Industry ◽  
Release Date ◽  
Data Mining Techniques ◽  
Box Office ◽  
Public Consumption ◽  
Using Data ◽  
Movie Success

The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.

scholarly journals A Potential Solution for Crop Yield Prediction by using Data Mining Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 1211-1214
Author(s):  
Mrs. S.S.N.L. Priyanka
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Ph Value ◽  
Machine Learning Algorithms ◽  
Potential Solution ◽  
Factors Affecting ◽  
Data Mining Techniques ◽  
Managerial Decisions ◽  
Using Data

Agriculture is undoubtedly the largest livelihood provider in India and also contributes a significant figure to the economy of our Country. The technological factors affecting the crop production includes practices used and also managerial decisions. So, predicting the crop yield prior to its harvest would help farmers to take appropriate steps. We attempt to resolve the issue by building a user-friendly prediction system. The results of the prediction are suggested to the farmer such that suitable changes can be made in order to improve the produce. There are different techniques or algorithms which help to predict crop yield. By analyzing all the parameters like location, soil nutrients, pH value, rainfall, moisture a potential solution can be obtained to overcome the situation faced by farmers. This paper focuses on the analysis of the agriculture data and finding optimal yield to provide an insight before the actual crop production using data mining techniques and Machine Learning algorithms.

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