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Phenotype Algorithm based Big Data Analytics for Cancer Diagnose.

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This study introduces a big data analytics approach using Hadoop Distributed File System (HDFS) and Natural Language Processing (NLP) for improved cancer diagnosis. The proposed phenotype techniques enhance classification accuracy, aiding early cancer detection and patient survival.

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Area of Science:

  • Big data analytics
  • Machine learning
  • Computational biology

Background:

  • Accurate and early cancer diagnosis is critical for patient survival, with later stages significantly reducing survival rates.
  • Managing and analyzing large volumes of patient data is essential for identifying patterns and improving diagnostic accuracy.
  • Existing machine learning algorithms face challenges in classifying cancer due to the sheer volume of patient data.

Purpose of the Study:

  • To propose a novel big data analytics framework for enhanced cancer diagnosis.
  • To improve the accuracy and efficiency of cancer patient data classification.
  • To leverage Hadoop Distributed File System (HDFS) and Natural Language Processing (NLP) for analyzing Electronic Health Records (EHR) and Electronic Medical Records (EMR).

Main Methods:

  • Utilized Hadoop Distributed File System (HDFS) and Two-Phase Map Reduce for data management and processing.
  • Employed phenotype techniques incorporating Natural Language Processing (NLP) tools to analyze diverse patient data.
  • Integrated gene mapping, age-related data, medical imaging, and patient histories into the analysis.
  • Applied a three-factorized model to calculate mean scores based on disease stage and pain status.

Main Results:

  • The proposed system demonstrates the highest performance in cancer diagnosis compared to existing methods.
  • Successfully handled and classified raw EHR and EMR data using phenotype techniques.
  • NLP tools effectively analyzed complex patient data, including gene mapping and medical imaging.

Conclusions:

  • The developed big data analytics approach significantly improves cancer diagnosis accuracy.
  • The integration of HDFS, Two-Phase Map Reduce, and NLP offers a robust solution for handling large-scale patient data.
  • This framework holds promise for earlier and more precise cancer detection, ultimately benefiting patient outcomes.