ArticleJournal of medical systems2016
A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study.JMIR medical informatics · 2026Article
- Article
- A unified approach for Parkinson's disease recognition: imbalance mitigation and grid search optimized boosting with LightGBM.Medical & biological engineering & computing · 2024Article
- Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers.Journal of Parkinson's disease · 2024Article
- Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations.Diagnostics (Basel, Switzerland) · 2022Review
- An Analysis of Vocal Features for Parkinson's Disease Classification Using Evolutionary Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
- Biological networks in gestational diabetes mellitus: insights into the mechanism of crosstalk between long non-coding RNA and NBMC pregnancy and childbirth · 2022Article
- Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance.Concurrency and computation : practice & experience · 2022Article
- Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson's Disease.Journal of healthcare engineering · 2022Article
- Vocal Feature Extraction-Based Artificial Intelligent Model for Parkinson's Disease Detection.Diagnostics (Basel, Switzerland) · 2021Article
- Bio-inspired dimensionality reduction for Parkinson's disease (PD) classification.Health information science and systems · 2020Article
- Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization.PloS one · 2020Article
- Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease.PloS one · 2017Article
- Medical Image Analysis by Cognitive Information Systems - a Review.Journal of medical systems · 2016Review
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
The use of machine learning tools has become widespread in medical diagnosis. The main reason for this is the effective results obtained from classification and diagnosis systems developed to help medical professionals in the diagnosis phase of diseases. The primary objective of this study is to improve the accuracy of classification in medical diagnosis problems. To this end, studies were carried out on 3 different datasets. These datasets are heart disease, Parkinson's disease (PD) and BUPA liver disorders. Key feature of these datasets is that they have a linearly non-separable distribution. A new method entitled k-medoids clustering-based attribute weighting (kmAW) has been proposed as a data preprocessing method. The support vector machine (SVM) was preferred in the classification phase. In the performance evaluation stage, classification accuracy, specificity, sensitivity analysis, f-measure, kappa statistics value and ROC analysis were used. Experimental results showed that the developed hybrid system entitled kmAW + SVM gave better results compared to other methods described in the literature. Consequently, this hybrid intelligent system can be used as a useful medical decision support tool.
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