Prediction of Erythemato-Squamous Diseases Using Machine Learning

Bukhari, Syed Nisar Hussain; Masoodi, Faheem; Dar, Muneer Ahmad; Wani, Nisar Iqbal; Sajad, Adfar; Hussain, Gousiya

doi:10.1201/9781003328780-6

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Precision (or positive prediction value) refers to the proportion of correct positive predictions out of all positive predictions made by the model, while recall value (or true positive rate or sensitivity) and FNR pertain to the ability of the trained model to accurately identify all the positive instances. The F1 score combines recall and precision and gives the number of times a correct prediction was made by the model [47,48].…”

Section: Performance Analysismentioning

confidence: 99%

Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals

Bukhari,

Ogudo

2024

Mathematics

View full text Add to dashboard Cite

The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, no diagnostic procedures exist for this disease. However, several computational approaches have proven successful in detecting PD at early stages, overcoming the disadvantages of traditional methods of diagnosis. In this study, a machine learning (ML) detection system based on the voice signals of PD patients is proposed. The AdaBoost classifier has been utilized to construct the model and trained on a dataset obtained from the machine learning repository of the University of California, Irvine (UCI). This dataset includes voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrated promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Furthermore, the robustness of the proposed model is rigorously assessed through cross-validation, revealing consistent performance across all iterations. The overarching objective of this study is to contribute to the scientific community by furnishing a robust system for the detection of PD.

show abstract

Section: Performance Analysismentioning

confidence: 99%