An automated pre-term prediction system using EHG signal with the aid of deep learning technique

From healthcare to banking, machine learning models are essential. However, their decision-making processes can be mysterious, challenging others who rely on their insights. The quality and kind of training and evaluation datasets determine these models' transparency and performance. This study examines how dataset factors affect machine learning model performance and interpretability. This study examines how data quality, biases, and volume affect model functionality across a variety of datasets. The authors find that dataset selection and treatment are crucial to transparent and accurate machine learning results. Accuracy, completeness, and relevance of data affect the model's learning and prediction abilities. Due to sampling practises or historical prejudices in data gathering, dataset biases can affect model predictions, resulting in unfair or unethical outcomes. Dataset size is also important, according to our findings. Larger datasets offer greater learning opportunities but might cause processing issues and overfitting. Smaller datasets may not capture real-world diversity, resulting in underfitting and poor generalisation. These views and advice are useful for practitioners. These include ways for pre-processing data to reduce bias, assuring data quality, and determining acceptable dataset sizes. Addressing these dataset-induced issues can improve machine learning model transparency and effectiveness, making them solid, ethical tools for many applications.

show abstract

Augmenting Chronic Kidney Disease Diagnosis With Support Vector Machines for Improved Classifier Accuracy

Kumar,

Gnanaguru

et al. 2024

Advances in Medical Technologies and Clinical Practice

View full text Add to dashboard Cite

Mitigating chronic kidney disease poses a substantial challenge to the healthcare community. This study assesses diverse classification algorithms, encompassing NaiveBayes, multi-layer perceptron, and support vector machine. The analysis involves scrutinizing the chronic kidney disease dataset from the UCI machine learning repository. Techniques like replacing missing values, unsupervised discretization, and normalization are employed for precision enhancement. The empirical results of the classification models are evaluated for accuracy and computational time. The conclusive observation indicates that the support vector machine performs notably better than all other classification methods, with a 76% classifier accuracy which is better than classifiers such as MLP and NB. The lack of application of those feature selection methods to the dataset is a drawback of this study.

show abstract

An automated pre-term prediction system using EHG signal with the aid of deep learning technique

Cited by 5 publications

References 24 publications

Predicting risk factors associated with preterm delivery using a machine learning model

Predicting risk factors associated with preterm delivery using a machine learning model

Shedding Light on Dataset Influence for More Transparent Machine Learning

Augmenting Chronic Kidney Disease Diagnosis With Support Vector Machines for Improved Classifier Accuracy

Contact Info

Product

Resources

About