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Research Article | Open Access
Volume 12 | Issue 12 | Year 2025 | Article Id. IJCSE-V12I12P101 | DOI : https://doi.org/10.14445/23488387/IJCSE-V12I12P101

Enhancing Diagnostics with Logistic Regression

Ruby Pilakhwal, Raj Trivedi, Ankita Patel, Dhruvi Pandya

Citation :

Ruby Pilakhwal, Raj Trivedi, Ankita Patel, Dhruvi Pandya, "Enhancing Diagnostics with Logistic Regression," International Journal of Computer Science and Engineering , vol. 12, no. 12, pp. 1-7, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I12P101

Abstract

Machine learning is significant in clinical decision support, especially regarding dermatological disorders, whose symptoms significantly overlap, and whose features are mostly discrete. The Logistic Regression (LR) can be employed in such a case, as it gives clear probabilistic results with which a clinician can work, but the literature in this field rarely presents a systematic comparison between LR and other classical algorithms in consistent assessment environments. This paper analyses the LR with the Dermatology dataset on the WEKA system and compares the performance with Naive Bayes, Support Vector (SMO) machine, and Decision Tree (J48). This work is novel due to its interpretability-based evaluation and the systematic analysis of classical models with the help of a discrete and symptom-based dataset of dermatology, a field where journal publications and literature tend to prioritize the precision of data over transparency or rigorous methods in data analysis. The study makes use of 10-fold cross-validation to examine the predictive accuracy and error behavior of each model in order to gain insight into the diagnostic potential of each model. Results indicate that LR has competitive accuracy and stable performance, but is interpretable, which is crucial in clinical applications. Even though SMO has a minor difference in accuracy, LR offers reliable and interpretable predictions, which apply to structured clinical data. The results provide an essential methodological foundation and emphasize the applicability of interpretable models to dermatology diagnostics, where there is a clear, understandable line of decision-making that can aid credible and reliable automated services.

Keywords

Classification, Data mining, Logistic regression, Machine learning, Weka.

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