Efficient Parkinson Diagnosis Method Using Handwriting and Firefly Feature Selection Algorithm.
Handwriting analysis is useful in a wide range of applications such as medical diagnostics. Artificial Intelligence (AI) methods have a vital role in assessing abnormalities using handwriting. This paper presents and evaluates an efficient handwriting-based Computer-Aided Diagnosis (CAD) system for...
| Publicado en: | Acupuncture & Electro-Therapeutics Research Vol. 50; no. 1; pp. 35 - 59 |
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| Autores principales: | , , , |
| Formato: | algorithm computer program pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190862211&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190862211 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03601293 1I5 jtl: Acupuncture & Electro-Therapeutics Research issn: 03601293 maglogo: N pubinfo: dt: Feb2026 vid: 50 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 190862211 187932218 190862211 190862211 10.1177/03601293251352642 190862211 ppf: 35 ppct: 24 formats: tig: atl: Efficient Parkinson Diagnosis Method Using Handwriting and Firefly Feature Selection Algorithm. aug: au: Soleimanidoust, Leila Rezai, Abdalhossein Barghamadi, Hamideh Ahanian, Iman affil: Department of Medical Engineering, ST.C. Islamic Azad University, Tehran, Iran sug: subj: Parkinson Disease Diagnosis Decision Support Systems, Clinical Automation Handwriting Evaluation Diagnosis, Computer Assisted Artificial Intelligence Human Image Processing, Computer Assisted Data Analysis Software Validity Sensitivity and Specificity Precision Correlation Coefficient Predictive Validity False Negative Results ROC Curve ab: Handwriting analysis is useful in a wide range of applications such as medical diagnostics. Artificial Intelligence (AI) methods have a vital role in assessing abnormalities using handwriting. This paper presents and evaluates an efficient handwriting-based Computer-Aided Diagnosis (CAD) system for Parkinson's diagnosis as one of the most common neurodegenerative diseases. The research objective is to improve the performance of the CAD system in Parkinson's diagnosis. The research strategy is using efficient AI methods. In the developed CAD system, the Gray-Level Co-occurrence Matrix (GLCM) is employed as a feature extraction method. The Firefly Algorithm (FA) is then applied to extracted features to select the most relevant features. The Support Vector Machine (SVM), k -Nearest Neighbor (kNN), and Ensemble algorithms are used to classify the results. The performance of the proposed CAD system is evaluated using MATLAB R2021b and a templated handwritten dataset collected at Botucatu Medical School, São Paulo State University, Brazil. The evaluation's findings show that the suggested method works well. The suggested CAD system performs best when the Meander handwritten exam, GLCM feature extraction method, FA feature selection algorithm, and SVM classification algorithm are used attaining an accuracy of 96%. The accuracy results in this study show that the proposed method can be considered a noninvasive accessible diagnostic method. pubtype: Academic Journal doctype: algorithm computer program pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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