Evaluation of Prostate Cancer via Machine Learning

Abstract

By training computers with machine learning technique, patients can be prevented from being exposed to unnecessarily difficult examinations. In recent years, machine learning-based disease assessment approach has gained importance in terms of the benefits it provides to clinical methods. There is a remarkable increase in studies in this direction. There are a limited number of clinical guiding parameters in predicting some types of cancer, and this limitation pushes the patients under treatment to a very frustrating process. For this reason, apart from ordinary procedure of the traditional medicine, an alternative approach to predict the any type of cancer is making a computer-based evaluation that has become a highly studied method in recent years. In this study, a machine learning (ML) approach will be used to evaluate prostate cancer, which is the second most common cancer-related death in men worldwide. For this purpose, the K-Nearest Neighbor (kNN) algorithm based on ML will be used with feature selection, which is a dimension reduction technique. An open source database, Kaggle, was used for the evaluation. The accuracy value of the used algorithm was found 88%.

Keywords

• Prostate cancer
• machine learning
• feature selection kNN.

Prostat Kanserinin Makine Öğrenimi Yoluyla Değerlendirilmesi

Abstract

Bilgisayarların makine öğrenimi tekniği ile eğitilmesi ile hastaların gereksiz yere zor tetkiklere maruz kalması engellenebilir. Son yıllarda makine öğrenimi tabanlı hastalık değerlendirme yaklaşımı, klinik yöntemlere sağladığı faydalar açısından önem kazanmıştır. Bu yönde yapılan çalışmalarda dikkat çekici bir artış vardır. Bazı kanser türlerini öngörmede sınırlı sayıda klinik yol gösterici parametre vardır ve bu kısıtlılık tedavi gören hastaları oldukça yıpratıcı bir sürece itmektedir. Bu nedenle, geleneksel tıbbın alışılagelmiş prosedürlerinden farklı olarak, herhangi bir kanser türünü tahmin etmede alternatif bir yaklaşım, son yıllarda üzerinde çok çalışılan bir yöntem haline gelen bilgisayar tabanlı değerlendirme yapmaktır. Bu çalışmada, dünya çapında erkeklerde ikinci en yaygın kansere bağlı ölüm olan prostat kanserini değerlendirmek için bir makine öğrenimi (ML) yaklaşımı kullanılacaktır. Bu amaçla bir boyut küçültme tekniği olan öznitelik seçimi ile ML için K-En Yakın Komşu (kNN) algoritması kullanılacaktır. Değerlendirme için açık kaynaklı bir veri tabanı olan Kaggle kullanılmıştır. Kullanılan algoritmanın doğruluk değeri %88 olarak elde edildi.

Keywords

• Prostat kanseri
• makine öğrenimi
• özellik seçimi
• kNN

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