Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models
- Title
- Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models
- Creator
- Nithya R.; Kokilavani T.; Beena T.L.A.
- Description
- In recent years, cerebral stroke has ascended as a paramount concern in global public health. Proactive strategies emphasizing metabolic control over salient risk factors present a superior approach compared to relying solely on physiological indicators, which may not delineate clear preventive directives. In this research, we present the SPX-CerebroPredict modela novel machine learning framework designed to classify imbalanced cerebral stroke data for clinical diagnostics. The study delves into feature selection methodologies, employing both information gain and principal component analysis (PCA). To address the class imbalance dilemma, the Synthetic Minority Over-sampling Technique (SMOTE) was harnessed. The empirical evaluation, conducted on the cerebral stroke prediction dataset from Kagglecomprising 43,400 medical records with 783 stroke instancespitted well-established algorithms such as support vector machine, logistic regression, decision tree, random forest, XGBoost, and K-nearest neighbor against one another. The results evince that our SPX-CerebroPredict model, integrating SMOTE, PCA, and XGBoost, surpasses its contemporaries, achieving an impressive accuracy rate of 95%. This discovery underscores the models potential for clinical applicability in cerebral stroke diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 449-462.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Cerebral stroke classification; Feature selection; Machine learning; Smote; Supervised learning
- Coverage
- Nithya R., Department of Computer Science, St. Josephs College (Autonomous), Affiliated to Bharathidasan University, Tamil Nadu, Tiruchirappalli, India; Kokilavani T., Department of Computer Science, Christ (Deemed to Be) University, Nagasandra, Yeshwanthpur Campus, Karnataka, Bangalore, India; Beena T.L.A., Department of Computer Science, St. Josephs College (Autonomous), Affiliated to Bharathidasan University, Tamil Nadu, Tiruchirappalli, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981970974-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Nithya R.; Kokilavani T.; Beena T.L.A., “Cerebral Stroke Classification Using Over Sampling Technique and Machine Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19400.