Early Prevention and Detection of Cancer Risk for Low Income Country using Data Mining Technology: Bangladesh Perspective

Shariful, Islam; Akhter, Sharmin; Salahuddin, Salahuddin; Sah, Jay Prakash; Rahman, Ramim Tanver; Asaduzzaman, Sayed; Ahmed, Kawsar; Mohiuddin, A. K. M.; Shibly, Abu Zaffar

doi:10.4172/2168-9652.1000e155

Cited by 2 publications

(3 citation statements)

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“…One of these new techniques is (medical) data mining, which can be defined as the non-trivial extraction of implicit previously unknown and potentially useful information or pattern about (medical) data [10,11]. Using this technology, the risk of cancer can be predicted, which may play a pivotal role in the diagnosis process and an effective preventive strategy or play a role in cancer screening [12]. Several classification models are proposed over the years such as artificial neural networks (ANNs), statistical models, decision trees, and genetic algorithm [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Using this technology, the risk of cancer can be predicted, which may play a pivotal role in the diagnosis process and an effective preventive strategy or play a role in cancer screening [12]. Several classification models are proposed over the years such as artificial neural networks (ANNs), statistical models, decision trees, and genetic algorithm [13,14]. ANN is a set of connected input/output units in which each connection has a weight associated with it.…”

Section: Introductionmentioning

confidence: 99%

“…They are also useful to explore data to gain insight into the relationships of a large number of candidate input variables to a target variable. The model can be designed in a couple of layers with which the accuracy of the model is adjusted [13,17]. C5.0 (after C5.0) is one of the decision tree algorithms (18) used/tested for different medical databases successfully or non-successfully.…”

Section: Introductionmentioning

confidence: 99%

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Applying Two Computational Classification Methods to Predict the Risk of Breast Cancer: A Comparative Study

Atashi

Sohrabi

Dadashi

2018

Multidiscip Cancer Investig

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Introduction: Lack of a proper method for early detection and diagnostic errors in medicine are some fundamental problems in treating cancer. Data analysis techniques may significantly help early diagnosis. The current study aimed at applying and evaluating neural networks and decision tree algorithm on breast cancer patients' data for early cancer prediction. Methods: In the current study, data from Breast Cancer Research Cancer (BCRC), ACE-CR (the Academic Center for Education, Culture and Research) were used consisting of data from 4004 patients with breast cancer risk factors. Of all records, 1642 (41%) were related to malignant changes and breast cancer and 2362 (59%) were related to benign tumors. Data were analyzed by neural networks perceptrons and decision tree algorithm and divided into two parts for training (70%) and testing (30%) using Rapid Miner 5.2. Results: For decision tree, accuracy of 81.62%, specificity of 79.80%, sensitivity of 89.49%, and for neural network, accuracy of 81.62%, specificity of 89.99%, and sensitivity of 90.80% were reported. Results showed acceptable capabilities to analyze breast cancer data for both algorithms. Conclusions: Although both models provided good results, neural network showed better diagnosis for positive cases. Database type and analysis method influenced the results. On the other hand, information about more powerful risk factors of breast cancer can provide models with high coverage.

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