scholarly journals Predictive Analysis using Data Mining Techniques for Heart Disease Diagnosis

2018 ◽  
Vol 7 (3.1) ◽  
pp. 166 ◽  
Author(s):  
Siddharth Joshi ◽  
Ashish Sasanapuri ◽  
Shreyash Anand ◽  
Saurav Nandi ◽  
Varsha Nemade
Keyword(s):  
Data Mining ◽  
Disease Diagnosis ◽  
Content Management ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Large Hospital ◽  
Use Of Data ◽  
Using Data ◽  
Heart Disease Diagnosis ◽  
Python Programming

Due to technological advancements in the field of computer science and data warehousing techniques. The healthcare industry ranging from small clinics to large hospital campuses use Content management system which has made the storage and accessing of data a faster option. But these large amounts of data generated are regrettably not mined and the data remains unexploited. Through this research we aim to demonstrate the use of Data Mining algorithm by using python programming language in order to create a desktop-based application which will cater to our aim. This Paper will analyze the performance by comparing the metrics of data analysis like accuracy, precision and recall in order introducing our software solution which tries to be more accurate than the work previously done on Cleveland, VA Hungarian data sets taken from UCI repository [1].  

USING DATA MINING FOR AUTOMATED SOFTWARE TESTING

2004 ◽  
Vol 14 (04) ◽  
pp. 369-393 ◽  
Author(s):  
M. LAST ◽  
M. FRIEDMAN ◽  
A. KANDEL
Keyword(s):  
Data Mining ◽  
Software Testing ◽  
Data Mining Algorithm ◽  
Test Automation ◽  
Automated Modeling ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Imperfect Knowledge ◽  
Using Data ◽  
Automated Software

In today's software industry, the design of test cases is mostly based on human expertise, while test automation tools are limited to execution of pre-planned tests only. Evaluation of test outcomes is also associated with a considerable effort by human testers who often have imperfect knowledge of the requirements specification. Not surprisingly, this manual approach to software testing results in heavy losses to the world's economy. In this paper, we demonstrate the potential use of data mining algorithms for automated modeling of tested systems. The data mining models can be utilized for recovering system requirements, designing a minimal set of regression tests, and evaluating the correctness of software outputs. To study the feasibility of the proposed approach, we have applied a state-of-the-art data mining algorithm called Info-Fuzzy Network (IFN) to execution data of a complex mathematical package. The IFN method has shown a clear capability to identify faults in the tested program.

scholarly journals Application of Data Mining Method Using Association Rules Apriori To Shopping Cart Analysis On Sale Transactions (Case Study Alfamidi Burnt Stone)

2020 ◽  
Vol 2 (2) ◽  
pp. 222-226
Author(s):  
Vanessa Siregar ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Cart Analysis ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Large Size ◽  
Using Data ◽  
Mining Algorithms

Also Often data mining is called knowledge discovery in databases (KDD), ie activities include the collection, historical use of data to find regularities, patterns or relationships in data sets with a large size. The company may be interested to know if some groups consistently goods items purchased together. This study analyzes the transaction of data information retrieval from the sale of skin care and hair care using data mining algorithms priori Alfamidi Burnt Stones with the highest support value is 8% and the highest value is 5% confidance

scholarly journals Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

2012 ◽  
Vol 32 (1) ◽  
pp. 184-196 ◽  
Author(s):  
Rubens A. C. Lamparelli ◽  
Jerry A. Johann ◽  
Éder R. dos Santos ◽  
Julio C. D. M. Esquerdo ◽  
Jansle V. Rocha
Keyword(s):  
Data Mining ◽  
Expectation Maximization ◽  
Spectral Behavior ◽  
Use Of Data ◽  
Spectral Profiles ◽  
Coffee Plants ◽  
Using Data ◽  
Hyperion Image ◽  
Noise Fraction ◽  
Behavior Profiles

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

scholarly journals An Efficient Clustering Approach for Automatic Detection of Calcification in Low Dose Chest CT

2019 ◽  
pp. 163-168
Author(s):  
P. Tamijiselvy ◽  
N. Kavitha ◽  
K. M. Keerthana ◽  
D. Menakha
Keyword(s):  
Data Mining ◽  
Low Dose ◽  
Early Stage ◽  
Clustering Algorithms ◽  
Automatic Detection ◽  
Chest Ct ◽  
Data Mining Algorithm ◽  
Fuzzy C Means ◽  
Data Mining Algorithms ◽  
Using Data

The degree of aortic calcification has been appeared to be a risk pointer for vascular occasions including cardiovascular events. The created strategy is fully automated data mining algorithm to segment and measure calcification using Low-dose Chest CT in smokers of age 50 to 70 .The identification of subjects with increased cardiovascular risk can be detected by using data mining algorithms. This paper presents a method for automatic detection of coronary artery calcifications in low-dose chest CT scans using effective clustering algorithms with three phases as Pre-Processing, Segmentation and clustering. Fuzzy C Means algorithm provides accuracy of 80.23% demonstrate that Fuzzy C means detects the Cardio Vascular Disease at early stage.

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