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Data mining, an interdisciplinary field bridging computer science as well as statistics, extracts insights using databases for the aid of decision-making. Within Data mining, classification involves learning from existing cases to predict the class of new cases. We investigate the performance of Decision Trees, ANN, KNN, Naive Bayes classifiers and SVM implemented in the Orange and WEKA platforms [1, 2]. Through comprehensive experimentation and evaluation, we assess the predictive capabilities and effectiveness of these ML techniques. The objective is to assist analysts in swiftly achieving effective results. This paper presents a thorough examination of accuracy calculation methodologies for assessing the performance of algorithms on data mining platforms.

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Comparative Analysis of Machine Learning Techniques for Cervical Cancer Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • B. Sarada,
  • A. Guru SSVS Murali Krishna,
  • P. Sarayu Sree Yadav,
  • K. Lakshmi Puspha,
  • S. Revathi,
  • Siva Sankar Namani

摘要

Data mining, an interdisciplinary field bridging computer science as well as statistics, extracts insights using databases for the aid of decision-making. Within Data mining, classification involves learning from existing cases to predict the class of new cases. We investigate the performance of Decision Trees, ANN, KNN, Naive Bayes classifiers and SVM implemented in the Orange and WEKA platforms [1, 2]. Through comprehensive experimentation and evaluation, we assess the predictive capabilities and effectiveness of these ML techniques. The objective is to assist analysts in swiftly achieving effective results. This paper presents a thorough examination of accuracy calculation methodologies for assessing the performance of algorithms on data mining platforms.