Cancer survivability prediction using random forest and rule induction algorithms

Alhaj, Majd A. M.; Maghari, Ashraf Y. A.

doi:10.1109/icitech.2017.8080031

Cited by 14 publications

(10 citation statements)

References 4 publications

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“…To forecast the survivability of tumor patients 2 cataloguing replicas are practical on tumor patient's records. By decision tree and naive Bayes algorithms an author build further classifiers in upcoming [11].…”

Section: B Kranthi Kiranmentioning

confidence: 99%

Implementation of Tumor Prediction System using Classification Algorithms

kiran

2019

IJEAT

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As the huge volume of healthcare data was being unused, recent researchers were focused on predicting the many diseases by analyzing the past patient records. In continuation with that, there are lot of researches focused on predicting the tumor on the human body. In this research, two widely used classification algorithms called Naïve Bayes and Random tree were considered for implementation and analysis with the UCI Machine learning Tumor data set. The data cleaning technique called “Replace Missing Values” in the WEKA tool has been considered for cleaning the data. The implementation has been done with the original dataset and the cleaned dataset. Finally, it is found that the Random tree algorithm is performed well with improved accuracy and reduced error rate. The accuracy obtained before data cleaning is 90.8333% and after data cleaning is 93.3333 %. Similarly, the error rates were reduced reasonably and they are 9.1667 % before data cleaning and 93.3333 % after data cleaning. In future, the data cleaning techniques has to be tuned well to improve the accuracy further.

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Section: B Kranthi Kiranmentioning

confidence: 99%

Implementation of Tumor Prediction System using Classification Algorithms

kiran

2019

IJEAT

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“…In the field area of Data Mining research, some research conducted on several works, such; Alduraibi et al, with their research using Rapidminer for predict the gold price movement using several algorithm Decission Tree, SVM, KNN, and linear regression [3], similar to the work done by Estrada developed models that automatically recognize postures by using a web camera with KNN, SVM, MLP [4]. Alhaj also built two classification models (Rule Induction and Random Forest) to predict the survivability of cancer patients of Gazastrip [5]. Cabral et al already made an analysis in field area of fraud detection system for electricity consumption based on data mining technique, they used Self-Organizing Maps (SOM) to gathering the data pattern.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced K-Means Clustering Algorithm for Pattern Discovery in Big Data Analysis of 3-Phase Electrical Quantities

Despa¹,

Nama²

2018

IJET

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The Unila Internet of Things Research Group (UIRG) was developed online monitoring of power distribution system based on Internet of Things (IoT) technology on Department of Electrical Engineering University of Lampung (Unila), has been running for several months, this system monitored electrical quantities of 3-phase main distribution panel of H-building. The measurement system involve multiple sensors such current sensors and voltage sensors, the measurement data stored in to database server and shown the information in a real-time through a web-based application.Main objective of this research was to capture, analyze, and identified the knowledge pattern of electrical quantities data measurements, using Cross-Industry Standard Process for Data Mining (CRISP-DM) data mining framework, for helping the stake holders to continuous improvement of the quality of electricity services, the initial research limited to total 770847 electrical quantities recorded data that save on database system, since 1 September - 31 October 2018, the dataset consist of 21 attribute electrical quantities such as; voltage, current, power factor values, energy consumption, frequency, on H building 3-Phase main panel control.Rapidminer as leading application on knowledge discovery application was used to analyze the big data, K-Mean cluster algorithm implemented to identify the data pattern, the result indicated that 3-Phase load was unbalanced, and Phase-0 was the most utilized phase, based on from total 5 cluster analysis result.

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“…Random forest is a learning method for classification or regression [40,41] that was proposed by Breiman in 2001 [41,42]. RF models consist of a classifier with different decision trees, where the final prediction is obtained by all the single classification trees [41,43], that is, for a quantitative response, the prediction is the average of each individual tree predicted value [44].…”

Section: Introductionmentioning

confidence: 99%

Prediction Models to Control Aging Time in Red Wine

et al. 2019

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A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur over the aging time can be used to distinguish between wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certify wines. In this research, different models have been developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit us to determine the aging time, with an average absolute percentage deviation below 1%, so it can be concluded that these two models have demonstrated their capacity to predict the age of wine.

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Cancer survivability prediction using random forest and rule induction algorithms

Cited by 14 publications

References 4 publications

Implementation of Tumor Prediction System using Classification Algorithms

Implementation of Tumor Prediction System using Classification Algorithms

An Enhanced K-Means Clustering Algorithm for Pattern Discovery in Big Data Analysis of 3-Phase Electrical Quantities

Prediction Models to Control Aging Time in Red Wine

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