EARLY LUNG CANCER DIAGNOSIS THROUGH A HIGH-PRECISION HYBRID MACHINE LEARNING APPROACH
Sandeep Singh Bains
, Chiman Saini
Keywords: Lung Cancer ,Hybrid Approach, Interpretability, Ensemble Methods.
Lung cancer, despite its strong association with smoking, can tragically strike young individuals who have never touched a cigarette. Predicting this risk in non-smokers presents a unique challenge for traditional methods due to the absence of the usual culprit. Machine learning (ML) emerges as a potential game-changer, holding the promise of early detection for these vulnerable patients.Current diagnostic tools often miss early-stage lung cancer in young non-smokers due to its atypical presentation. Subtle symptoms like fatigue, cough, or shortness of breath might be overlooked, leading to delayed diagnosis and poorer prognosis. This calls for a proactive approach, where ML models can leverage diverse risk factors beyond smoking to predict the likelihood of cancer development.A hybrid approach, combining different ML techniques, holds the key to unlocking greater accuracy in risk prediction. We propose a hybrid approach that leverages the strengths of various ML algorithms to overcome individual limitations.The approach entails Data Preprocessing which is Employing efficient techniques to handle missing values, address imbalanced classes, and perform necessary dimensionality reduction. Feature Selection that Implementing filter and wrapper methods to identify the most relevant features for accurate prediction.Hybrid Model development that Combining complementary ML algorithms (e.g., ensemble methods, Support Vector Machines, Logistic Regression) .Performance Evaluation that employing comprehensive metrics like accuracy, precision, recall, and F1-score to assess and compare the performance of individual and hybrid models.
"EARLY LUNG CANCER DIAGNOSIS THROUGH A HIGH-PRECISION HYBRID MACHINE LEARNING APPROACH", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.a308-a314, February-2024, Available :https://tijer.org/TIJER/papers/TIJER2402038.pdf
Volume 11
Issue 2,
February-2024
Pages : a308-a314
Paper Reg. ID: TIJER_150962
Published Paper Id: TIJER2402038
Downloads: 000279
Research Area: Science and Technology
Country: Mohali, Punjab, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
