scholarly journals Diagnosis of Diabetes by using Data Mining Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 2368-2372
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Linear Regression ◽  
Parameter Tuning ◽  
The Other ◽  
Classification Methods ◽  
Data Mining Techniques ◽  
Best Parameter ◽  
Using Data

There are many classifiers that are used for diagnosis of diabetes but the result of this paper shows that how logistic regression having best accuracy among the other classifiers. Logistic regression removes the disadvantages of linear regression. There are different classifiers that are used for prediction. In the worldwide millions of peoples are suffering from diabetes according to WHO report. In the medical region, many researches have done with the help of data mining. The aim of this paper is to diagnosis of diabetes by using the best classifiers and providing best parameter tuning. The study helps to find whether a patient is enduring from diabetes or not using classification methods and it further investigate and evaluates the functioning of different classification in relations of precision, accuracy, recall & roc

scholarly journals Towards reliable prediction of academic performance of architecture students using data mining techniques

2018 ◽  
Vol 16 (3) ◽  
pp. 385-397 ◽  
Author(s):  
Ralph Olusola Aluko ◽  
Emmanuel Itodo Daniel ◽  
Olalekan Shamsideen Oshodi ◽  
Clinton Ohis Aigbavboa ◽  
Abiodun Olatunji Abisuga
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Logistic Regression ◽  
Academic Performance ◽  
Predictive Accuracy ◽  
Support Vector ◽  
Content Type ◽  
Data Mining Techniques ◽  
Svm Model ◽  
Using Data

Purpose In recent years, there has been a tremendous increase in the number of applicants seeking placements in undergraduate architecture programs. It is important during the selection phase of admission at universities to identify new intakes who possess the capability to succeed. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during the selection process. This paper aims to investigates the efficacy of using data mining techniques to predict the academic performance of architecture students based on information contained in prior academic achievement. Design/methodology/approach The input variables, i.e. prior academic achievement, were extracted from students’ academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data were divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. Findings The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement is a good predictor of academic performance of architecture students. Research limitations/implications Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. Originality/value The developed SVM model can be used as a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

scholarly journals Predicting Breast Cancer Survivability

2020 ◽  
Vol 4 (1) ◽  
pp. 17-30
Author(s):  
Omead I. Hussain
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Decision Trees ◽  
Modeling Tools ◽  
Data Mining Techniques ◽  
Artificial Neural ◽  
Using Data

this study concentrates on Predicting Breast Cancer Survivability using data mining, and comparing between three main predictive modeling tools. Precisely, we used three popular data mining methods: two from machine learning (artificial neural network and decision trees) and one from statistics (logistic regression), and aimed to choose the best model through the efficiency of each model and with the most effective variables to these models and the most common important predictor. We defined the three main modeling aims and uses by demonstrating the purpose of the modeling. By using data mining, we can begin to characterize and describe trends and patterns that reside in data and information. The preprocessed data set contents were of 87 variables and the total of the records are 457,389; which became 93 variables and 90308 records for each variable, and these dataset were from the SEER database. We have achieved more than three data mining techniques and we have investigated all the data mining techniques and finally we find the best thing to do is to focus about these data mining techniques which are Artificial Neural Network, Decision Trees and Logistic Regression by using SAS Enterprise Miner 5.2 which is in our view of point is the suitable system to use according to the facilities and the results given to us. Several experiments have been conducted using these algorithms. The achieved prediction implementations are Comparison-based techniques. However, we have found out that the neural network has a much better performance than the other two techniques. Finally, we can say that the model we chose has the highest accuracy which specialists in the breast cancer field can use and depend on.

scholarly journals Novel Method for Cricket Match Outcome Prediction using Data Mining Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 15-21
Keyword(s):  
Data Mining ◽  
Outcome Prediction ◽  
Data Models ◽  
The Other ◽  
Data Mining Techniques ◽  
Key Players ◽  
The Future ◽  
Novel Method ◽  
Bayes Algorithm ◽  
Using Data

Cricket is one of the most popular games in many countries. As many as 19 countries play cricket as their main game, and the number is likely to increase in the future. However, there are no suitable tools for analyzing pre-outcome of the match from beginning to end. Existing tools do not support simulating match using batting partnerships. The ultimate goal of predicting pre-outcome of a cricket match is to identify key players and their batting performances. It is also to prevent wrong players from selecting and toss decision by making statistical predictions. This research focuses on One Day International (ODI) cricket match and predicts the outcome of a particular match. Our solution consists of three major modules, namely, Web UI Module, CRIC-Win Analytic Engine, and Backend Data Module. CRIC-Win Analytic Engine has two sub data models, one for predicting the overall match outcome based on a given pre-match data, and the other for predicting match outcome based on batting partnership of both home and rival teams. All sub-models in the CRIC-Win Analytic Engine are developed using the Naïve Bayes algorithm for generating the classifier model, which is used to predict the outcome of the cricket match.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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