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Journal : International Journal of Advances in Intelligent Informatics

Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE Unjung, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1627

Abstract

Parkinson's disease is one of the major neurodegenerative diseases that affect the central nervous system, often leading to motor and cognitive impairments in affected individuals. A precise diagnosis is currently unreliable, plus there are no specific tests such as electroencephalography or blood tests to diagnose the disease. Several studies have focused on the voice-based classification of Parkinson's disease. These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. This research aims to improve the accuracy of speech-based Parkinson's disease prediction by addressing class imbalance in the data and building an appropriate model. The proposed new model is to perform class balancing using SMOTE and build an ensemble voting model. The research process is systematically structured into multiple phases: data preprocessing, sampling, model development utilizing a voting ensemble approach, and performance evaluation. The model was tested using voice recording data from 31 people, where the data was taken from OpenML. The evaluation results were carried out using stratified cross-validation and showed good model performance. From the measurements taken, this study obtained an accuracy of 97.44%, with a precision of 97.95%, recall of 97.44%, and F1-Score of 97.56%. This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy.
Co-Authors Abas Setiawan Adha, Nugraha Saputra Adi, Pungky Tri Kisworo Adi, Pungky Tri Kisworo Afifah, Eka Nur Afifah, Eka Nur Ahmad Solikhin Gayuh Raharjo Al Hakim, M. Faris Alamsyah - Amin Suyitno Anggara, Dian Christopher Anggyi Trisnawan Putra Arief Broto Susilo Astuti, Winda Try Astuti, Winda Try Asyrofiyyah, Nuril Atikah Ari Pramesti, Atikah Ari Auni, Ahmad Ramadhan Budi Prasetiyo, Budi Choirunnisa, Rizkiyanti Clarissa Amanda Josaputri, Clarissa Amanda Devi, Feroza Rosalina Devi, Feroza Rosalina Dian Tri Wiyanti Dwijanto Dwijanto, Dwijanto Dwika Ananda Agustina Pertiwi Fitriana, Erma Nurul Florentina Yuni Arini, Florentina Yuni Hani'ah, Ulfatun Hariyanto, Abdul Heryadi, Muhammad Heri Isa Akhlis Juliater Simamarta Jumanto Unjung Korzhakin, Dian Alya Krida Singgih Kuncoro Kurniawati, Putri Aida Nur Lestari, Dewi Indah Listiana, Eka Malisan, Johny Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Much Aziz Muslim Muhammad Kharis Mulyono Mulyono Mutiara Hernowo Muzayanah, Rini Nofrisel, Nofrisel Oktaria Gina Khoirunnisa Perbawawati, Anna Adi Perbawawati, Anna Adi Pipit Riski Setyorini Pradana, Dany Pradhana, Fajar Eska Purnamasari, Ratnaningtyas Widyani Ratri Rahayu Riza Arifudin Rizki Danang Kartiko Kuncoro Rofik Rofik, Rofik Rupiah, Siti S.Pd. M Kes I Ketut Sudiana . Sampurno, Global Ilham Sampurno, Global Ilham Sari, Firar Anitya Sekarwati Ariadi, Tiara Subarkah, Agus Sukestiyarno Sukestiyarno Sukmadewanti, Irahayu Sukmadewanti, Irahayu Sulis Eli Triliani, Sulis Eli Supriyono Supriyono Susanti, Eka Lia Sutarti, Sri Sutarti, Sri Umi Latifah Vedayoko, Lucky Gagah Vedayoko, Lucky Gagah Whisnu Ulinnuha Setiabudi, Whisnu Ulinnuha Wijaya, Henry Putra Imam Zaaidatunni'mah, Untsa Zaenal Abidin