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Erschienen in:
Analysis of Link Prediction in Directed and Weighted Social Network Structure Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Breast Cancer Diagnosis and Prognosis Using Machine Learning Techniques

verfasst von : Sunil Suresh Shastri, Priyanka C. Nair, Deepa Gupta, Ravi C. Nayar, Raghavendra Rao, Amritanshu Ram

Erschienen in: Intelligent Systems Technologies and Applications

Verlag: Springer International Publishing

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Abstract

Breast cancer is one of the major type of cancer which is the leading cause of death in women. The research work is carried out on the real data of patient records obtained from HealthCare Global Enterprises Ltd (HCG) hospitals. The work analyzes the four major class variables in the dataset, namely death, progression, recurrence and metastasis. The influence of the same 11 predictor variables is explored for each of the class. Various machine algorithms namely Support Vector Machine, Decision Tree, Multi-layer Perceptron and Naive Bayes have been explored for classification of the patient data into various classes. The imbalance in the data is handled using an over sampling technique. The contribution of various attributes in classifying the instances into different classes is also being explored. The model helps in predicting various factors and thus helps in early diagnosis in the breast cancer.

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Vorheriges Kapitel Empirical Comparison of Different Key Frame Extraction Approaches with Differential Evolution Based Algorithms
Nächstes Kapitel Performance Assessment Framework for Computational Models of Visual Attention
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Metadaten
Titel
Breast Cancer Diagnosis and Prognosis Using Machine Learning Techniques
verfasst von
Sunil Suresh Shastri
Priyanka C. Nair
Deepa Gupta
Ravi C. Nayar
Raghavendra Rao
Amritanshu Ram
Copyright-Jahr
2018
Buch
Intelligent Systems Technologies and Applications

Print ISBN: 978-3-319-68384-3

Electronic ISBN: 978-3-319-68385-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-68385-0_28