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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Leukocytes01:30

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Related Experiment Video

Updated: Aug 4, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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An integrative machine learning framework for classifying SEER breast cancer.

P Manikandan1, U Durga2, C Ponnuraja3

  • 1Department of Data Science, Loyola College, Chennai, 600 034, India. manimkn89@gmail.com.

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Summary
This summary is machine-generated.

This study used machine learning to predict breast cancer patient survival. The Decision Tree algorithm achieved 98% accuracy, outperforming other methods for the SEER dataset.

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Area of Science:

  • Oncology
  • Biomedical Informatics
  • Machine Learning

Background:

  • Breast cancer is a leading cause of mortality in women globally.
  • Accurate prediction of patient survival is crucial for effective treatment strategies.
  • Machine learning offers powerful tools for analyzing complex biomedical datasets.

Purpose of the Study:

  • To classify the survival status (alive/deceased) of breast cancer patients.
  • To develop a machine learning model using the Surveillance, Epidemiology, and End Results (SEER) dataset.
  • To identify the most effective machine learning algorithm for breast cancer patient classification.

Main Methods:

  • Data pre-processing and visualization of the SEER breast cancer dataset.
  • A two-step feature selection process using Variance Threshold and Principal Component Analysis.
  • Classification using supervised and ensemble learning algorithms: Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes, and Decision Tree.
  • Performance evaluation using train-test split and k-fold cross-validation.

Main Results:

  • The Decision Tree algorithm achieved the highest accuracy of 98% for predicting patient survival.
  • This accuracy was consistent across both train-test split and k-fold cross-validation methods.
  • The Decision Tree model demonstrated superior performance compared to other evaluated algorithms.

Conclusions:

  • Machine learning, particularly the Decision Tree algorithm, provides a feasible and highly accurate approach for classifying breast cancer patient survival status.
  • The feature selection method effectively reduced dimensionality while retaining important predictive information.
  • The findings suggest that Decision Tree can be a valuable tool in breast cancer prognosis and patient management.