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Journals / The Journal of Cognitive Systems / 2020 / Volume: 5 Issue: 2

ASSOCIATIVE CLASSIFICATION APPROACH CAN PREDICT PROSTATE CANCER BASED ON THE EXTRACTED ASSOCIATION RULES

İpek Balıkçı Çiçek(İNÖNÜ ÜNİVERSİTESİ, TIP FAKÜLTESİ, TEMEL TIP BİLİMLERİ BÖLÜMÜ, BİYOİSTATİSTİK VE TIP BİLİŞİMİ ANABİLİM DALI)0000-0002-3805-9214
Zeynep Küçükakçalı(İNÖNÜ ÜNİVERSİTESİ, TIP FAKÜLTESİ)0000-0001-7956-9272
Cemil Çolak(İNÖNÜ ÜNİVERSİTESİ, TIP FAKÜLTESİ, TEMEL TIP BİLİMLERİ BÖLÜMÜ, BİYOİSTATİSTİK VE TIP BİLİŞİMİ ANABİLİM DALI)0000-0001-5406-098X
Pages
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Publication Date
2020-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: In the analysis of the open-access data set, the proposed associative classification model has distinctively successful results in classifying prostate cancer on the performance metrics.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Aim: This study aims to classify the diagnosis status of prostate cancer and determine the related factors by applying the associative classification method, one of the data mining methods, to the open-access prostate cancer data set.
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Key Findings & Data:The performance of the associative classification model is evaluated using the classification performance metrics such as sensitivity, selectivity, accuracy, balanced accuracy, negative predictive value, positive predictive value, and F1-score.
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Conclusion & Impact:Conclusion: In the analysis of the open-access data set, the proposed associative classification model has distinctively successful results in classifying prostate cancer on the performance metrics.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Prostate cancer · data mining · association rules

Abstract

Aim: This study aims to classify the diagnosis status of prostate cancer and determine the related factors by applying the associative classification method, one of the data mining methods, to the open-access prostate cancer data set. Materials and Methods: In the current study , an open-access data set named "Prostate Cancer" is used for classification. The performance of the associative classification model is evaluated using the classification performance metrics such as sensitivity, selectivity, accuracy, balanced accuracy, negative predictive value, positive predictive value, and F1-score. Results: According to the prostate cancer classification results obtained from the associative classification model, the accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score values were obtained as 0.968, 0.789, 0.9, 0.879, 0.938, 0.882 and 0.923, respectively. Conclusion: In the analysis of the open-access data set, the proposed associative classification model has distinctively successful results in classifying prostate cancer on the performance metrics.

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Cite This Article

Çiçek, İ. B., Küçükakçalı, Z., Çolak, C. (2020). ASSOCIATIVE CLASSIFICATION APPROACH CAN PREDICT PROSTATE CANCER BASED ON THE EXTRACTED ASSOCIATION RULES. The Journal of Cognitive Systems, Volume: 5 Issue: 2. https://ajindex.com/articles/1551983

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