Author Login IJIRT Partner Network (IPN) Login

Comparative Analysis of Lung Diseases Using Different Machine Learning Algorithms

  • Unique Paper ID: 169219
  • Volume: 11
  • Issue: 6
  • PageNo: 836-843
  • Abstract:
  • The creation of automated methods for pneumonia early diagnosis has garnered substantial interest due to its rising frequency, especially in vulnerable groups. To assess how well four well-known machine learning (ML) algorithms diagnose pneumonia using chest X-ray pictures, this study compares them: Neural networks with convolutional properties, artificial neural properties, random forests, and support vector machines. The tagged photos in the dataset utilized in this study were classified into classes that were positive and negative for pneumonia and were obtained from Kaggle. The efficacy of each method was evaluated by a comprehensive process of implementation and optimization that involved the measurement of computing efficiency, accuracy, precision, recall, and F1 score, among other metrics. It was discovered that the CNN model, which is highly recognized for its proficiency in image classification tasks. However, the ANN exhibited competitive performance after hyperparameter tuning, providing a balance between complexity and interpretability. The RF algorithm, while less computationally intensive, showed limitations in image-based diagnosis due to its dependence on feature engineering. The SVM, known for its generalization capabilities, performed reasonably well but struggled with larger datasets and complex image features. This study not only highlights the strengths and weaknesses of these algorithms but also discusses the clinical relevance of the findings, especially concerning the need for fast and accurate diagnostic tools in healthcare settings. Our analysis concludes that while CNN remains the most effective algorithm for pneumonia detection in this context, the use of ensemble models or hybrid approaches could potentially improve diagnostic accuracy and robustness in future applications. The paper provides valuable insights for healthcare professionals and researchers aiming to integrate AI-driven diagnostic tools into clinical workflows.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 6
  • PageNo: 836-843

Comparative Analysis of Lung Diseases Using Different Machine Learning Algorithms

Related Articles

09 Apr, 2024

Enhanced Speech Recognition & Abstractive Text Summarisation with wav2vec2 & Pegasus

Read More
Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry