Healthcare Analytics: Overcoming the Barriers to Health Information Using Machine Learning Algorithms

Veena, A.; Gowrishankar, S.

doi:10.1007/978-3-030-51859-2_44

Cited by 3 publications

References 45 publications

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Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

Vijayakumar¹

2021

JIIP

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Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves the accuracy of biometric system. In many developed countries, palm print features are employed to provide the most accurate identification of an actual individual as fast as possible. The proposed system can be very suitable for the person who dislikes answering many questions for security authentication. Moreover, the proposed system can also be used to minimize the extra questionnaire by achieving a highest accuracy than other existing multimodal biometric systems. Finally, the results are computed and tabulated in this research article.

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Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

Vijayakumar¹

2021

JIIP

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Shedding Light on Dataset Influence for More Transparent Machine Learning

Settibathini,

Virmani,

Kuppam

et al. 2024

Explainable AI Applications for Human Behavior Analysis

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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.

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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

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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.

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Healthcare Analytics: Overcoming the Barriers to Health Information Using Machine Learning Algorithms

Cited by 3 publications

References 45 publications

Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

Shedding Light on Dataset Influence for More Transparent Machine Learning

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

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