Forthcoming and Online First Articles

International Journal of Functional Informatics and Personalised Medicine

International Journal of Functional Informatics and Personalised Medicine (IJFIPM)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Functional Informatics and Personalised Medicine (1 paper in press)

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  • Predicting and Analyzing Breast Cancer tumors by adopting Hybrid Neuro Fuzzy approach   Order a copy of this article
    by Sanjiban Sekhar Roy, Saptarshi Chakraboty, P.V. Krishna, Ajith Abraham 
    Abstract: It is often observed and criticized that health experts demonstrate variability in treatment while making crucial decisions regarding the diagnosis of a patient. These variations of decision making not only depends upon the condition of the patient; but also the doctors experience in that particular field plays a huge part in such crucial decision making. To solve these types of problem, researchers and medical experts have suggested setting a standard set of parameters to analyze the different conditions. Automated (computerized) models were also proposed as these models dont exhibit this variability while making crucial decisions. The benefit of computerized model is its consistency and accuracy in decision making. In this paper, we have proposed a hybrid model to simulate a breast cancer treatment scenario based on the medical history of the patients stored in the hospital database. Our proposed approach can detect whether the tumor is benign or malignant based on the assessment of nine different biological attributes. We have tested the performance of our proposed model on the Wisconsin Breast Cancer Database and compared the result obtained from our proposed approach with the other existing machine learning techniques. Through different experiments, we have established the effectiveness of our proposed algorithm.
    Keywords: Neuro-Fuzzy; Breast Cancer; Hybrid Method; ANFIS.

 

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