scholarly journals Disease Prediction by Machine Learning Over Big Data Lung Cancer

2021 ◽  
pp. 16-24
Author(s):  
T. Shanmuga Priya ◽  
T. Meyyappan
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Genetic Factors ◽  
Medical Information ◽  
Developed Countries ◽  
Classification Algorithms ◽  
Prediction System ◽  
Lung Cancer Death ◽  
Cancer Prediction ◽  
Data Mining Approach

Lung Cancer is one of the deadly diseases in the world today. Lung Cancer is caused because of some genetic factors and/or environmental factors and/or today’s modern lifestyle. Lung cancer has become the primary reason of death in developed countries. The majority effective way to decrease lung cancer death is to detect it earlier. The in advance detection of cancer is not easier method but if it is detecte it is curable. Various works have been done in predicting lung cancer different data mining approach and algorithm were adopt by different people. All work has some limits such as lack of intelligent prediction, and incompetent in structure that forced to take up this problem and to implement the Data mining based cancer prediction System (DMBCPS). This has proposed the Lung cancer prediction system based on data mining. This system is validated by comparing its predicted results with patient’s prior medical information and it was analyzed by using weka tool system. We analyzed the lung cancer prediction using classification algorithm such as Naive Bayes, SVM and Random forest algorithm. The dataset have 782 instances and 31 attributes. The main aim of this paper is to provide the earlier warning to the users and the performance analysis of the classification algorithms.

scholarly journals P1.17-01 Robustness of an Image-Based Data Mining Approach in Lung Cancer Patients

2018 ◽  
Vol 13 (10) ◽  
pp. S654-S655 ◽  
Author(s):  
E. Vasquez Osorio ◽  
M. Aznar ◽  
A. Green ◽  
C. Faivre-Finn ◽  
M. Van Herk ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Cancer Patients ◽  
Lung Cancer Patients ◽  
Data Mining Approach

Data Mining Approach to Classify Cases of Lung Cancer

2021 ◽  
pp. 511-521
Author(s):  
Eduarda Vieira ◽  
Diana Ferreira ◽  
Cristiana Neto ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Lung Cancer ◽  
Data Mining ◽  
Data Mining Approach

scholarly journals Predicting Paediatric Malaria Occurrence Using Classification Algorithm in Data Mining

2019 ◽  
pp. 1-10 ◽  
Author(s):  
T. C. Olayinka ◽  
S. C. Chiemeke
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Method ◽  
Developed Countries ◽  
Classification Algorithms ◽  
Data Sets ◽  
Tree Model ◽  
Healthcare Data ◽  
Paediatric Malaria ◽  
The Developed Countries

This paper gives the current overview of the application of data mining techniques on the haematological and biochemical dataset to predict the occurrence of malaria in children between age zero (0) and five (5).  Malaria has been eradicated from the developed countries but still affecting a large part of the world negatively. A larger percentage of malaria is estimated to affect young children in sub-Sahara Africa.  In order to reduce mortality from paediatric malaria, there should be an efficient and effective prediction method.  In healthcare, data mining is one of the most vital and motivating areas of research with the objective of finding meaningful information from huge data sets and provides an efficient analytical approach for detecting unknown and valuable information in healthcare data.  In this study, a model was built to predict the occurrence of malaria in children between age zero (0) and five (5) years, using decision tree classification algorithms on WEKA workbench tool.  The classification algorithms used are LMT, REPTree, Hoeffding tree and J48. A J48 algorithm was used for building the decision tree model since it has higher accuracy for performance with least error margin.

scholarly journals Data Mining Approach of Accident Occurrences Identification with Effective Methodology and Implementation

2018 ◽  
Vol 8 (5) ◽  
pp. 4033 ◽  
Author(s):  
Meenu Gupta ◽  
Vijender Kumar Solanki ◽  
Vijay Kumar Singh ◽  
Vicente García-Díaz
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Structural Data ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Research Approach ◽  
Word Count ◽  
Data Mining Approach ◽  
Frequency Calculation

Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.

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