Parkinson disease prediction using feature selection technique in machine learning

Wasif, Tamim; Hossain, Md. Inzamam Ul; Mahmud, Asif

doi:10.1109/icccnt51525.2021.9580151

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…While effective, wrapper methods can be computationally expensive due to repeated model training. 20 , 21 , 22 , 23 Embedded methods seamlessly integrate feature selection into the model training process, selecting features based on their relevance to model performance. Techniques like Lasso regression and decision trees employ embedded feature selection, offering computational efficiency well-suited for larger datasets.…”

Section: Methodsmentioning

confidence: 99%

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

Halder,

Uddin,

Uddin

et al. 2024

Journal of Pathology Informatics

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Section: Methodsmentioning

confidence: 99%

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

Halder,

Uddin,

Uddin

et al. 2024

Journal of Pathology Informatics

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“…Because the classifier operates under the strong features independence assumption, it is termed naive. There are several variations of NB found in the literature [19], with the key distinction being how the probability of the target class is calculated. These variations include simple Naive Bayes, Gaussian Naive Bayes (was used here in this study), Multinomial Naive Bayes, Bernoulli Naive Bayes, and Multi-variant Poisson Naive Bayes.…”

Section: ) Naive Bayesmentioning

confidence: 99%

Classification of Parkinson’s Disease using Speech Signal with Machine Learning and Deep Learning Approaches

Rahman

Hasan

Sarkar

et al. 2023

EJECE

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Parkinson's disease (PD) is a chronic neurological condition that is growing in prevalence and manifests both motor and non-motor symptoms. Most PD patients have trouble speaking, writing, and walking during the early stages of the disease. Analysis of speech problems has been effective in identifying Parkinson's disease. According to studies, 90% of Parkinson's disease patients experience speech problems. Even though there is no known cure for Parkinson's disease, using the right medication at an early stage can greatly reduce the symptoms. One of the key categorization issues for the diagnosis of Parkinson's disease is the correct interpretation of speech signals. The major goal of this project is to use deep learning and machine learning approaches to predict and categorize PD patients at an early stage. A trustworthy dataset from the UCI repository for Parkinson disease has been used to evaluate the method's performance. Several classification models are successfully used in this study for classification tasks, including Extreme Gradient Boosting (XGBoost), Ada Boost, Light Gradient Boosting Machine, CatBoost, Gradient Boosting, Random Forest, Ridge, Decision Tree, Logistic Regression, K Neighbors, SVM - Linear Kernel, Naive Bayes, and deep neural networks (DNN1, DNN2, DNN3). The Extreme Gradient Boosting classifier achieved the greatest classification accuracy of 92.18% (among the machine learning classifiers). By using the chosen features as input, the three layer deep neural network (DNN2) has the best accuracy of 95.41% amongst deep learning techniques. The collected results indicate that deep neural networks performed better than machine learning methods.

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“…The classifier is referred to as naive because it operates under the strong features independence assumption. The key distinction between the various versions of NB found in the literature [11] is, how the likelihood of the intended class is computed. Simple Naive Bayes, Gaussian Naive Bayes (which was used in this study), Multinomial Naive Bayes, Bernoulli Naive Bayes, and Multi-variant Poisson Naive Bayes are some of these variations.…”

Section: 12naïve Bayesmentioning

confidence: 99%

Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

-¹,

-²,

-³

2023

IJFMR

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Deep Learning (DL) and Machine Learning (ML) have the great prospect to play a significant role in the medical field in disease prediction. The tumor or cancer is one of the major health issues that each nation is currently dealing with, and it is the topic of this essay. The prediction of unidentified primary tumors in the dataset is delineated in this paper. Given that it provides significantly higher accuracy than binary classifiers, different multiclass classifier such as K-Nearest Neighbor (KNN), CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, Light Gradient Boosting Machine, Ada Boost Classifier, Decision Tree Classifier, SVM - Linear Kernel, Naive Bayes and Deep neural networks (DNN1, DNN2, and DNN3) are used to categorize multiclass datasets available in the UCI machine learning repository. Among the stated machine learning classifiers, the k-Nearest Neighbor (KNN) had the highest classification accuracy of 92.92%. The three layer deep neural network (DNN2), among deep learning techniques, had produced the best accuracy of 97.66% using the chosen features as input. The gathered results from this work showed that deep neural networks outperformed machine learning techniques.

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Parkinson disease prediction using feature selection technique in machine learning

Cited by 6 publications

References 4 publications

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

Classification of Parkinson’s Disease using Speech Signal with Machine Learning and Deep Learning Approaches

Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

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