Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder

Jones, Anna Hendri Soleliza; Hardiyanti, Cicin

doi:10.30645/ijistech.v5i1.111

Cited by 3 publications

(3 citation statements)

References 7 publications

(7 reference statements)

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“…The training procedure is time-consuming due to the slow learning process. The similarity between data points is evaluated using metrics such as Euclidean distance or equivalent distance measures ( Jones & Hardiyanti, 2021 ).…”

Section: Proposed Methodologymentioning

confidence: 99%

Heart failure survival prediction using novel transfer learning based probabilistic features

Qadri,

Hashmi,

Raza

et al. 2024

PeerJ Computer Science

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Heart failure is a complex cardiovascular condition characterized by the heart’s inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

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Section: Proposed Methodologymentioning

confidence: 99%

Heart failure survival prediction using novel transfer learning based probabilistic features

Qadri,

Hashmi,

Raza

et al. 2024

PeerJ Computer Science

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“…This method has good reliability in finding multiple images. The Euclidean distance is calculated as [10],…”

Section: A Candidate Set Of Feature Vectorsmentioning

confidence: 99%

3D Facial Expression Recognition Based on Geometric Feature Fusion

Quan Li

2024

jes

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In order to overcome the problems of missing important information and di-mensionality disaster of three-dimensional (3D) facial expression, a novel method based on geometric feature fusion is proposed. This approach initially extracts distance feature vectors and angle feature vectors from key facial regions such as the eyes, mouth, and eyebrows using direct geometric feature. Subsequently, the K-nearest neighbor (K-NN) algorithm is used to obtain the distance feature vector candidate sets and angle feature vector candidate sets separately. Finally, the maximum-minimum rule is utilized to fuse these candidate sets into a single feature vector, completing the recognition of 3D facial expression. Experimental results on the BU-3DFE database demonstrate that this method achieves on overall recognition rate of 97.6%, exhibiting excellent robustness to varia-tions in facial expressions. Furthermore, this approach can serve as a valuable reference for future research in the field of 3D face recognition.

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“…The training time is slow due to lazy learning. The data point's similarity is calculated by using Euclidean distance or similar other distance metrics [51].…”

Section: Applied Learning Techniquesmentioning

confidence: 99%

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach

Raza

Rustam

Siddiqui

et al. 2022

Genes

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Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions to genetic disorder prediction. A novel feature engineering approach is proposed where the class probabilities from an extra tree (ET) and random forest (RF) are joined to make a feature set for model training. Secondly, the study utilizes the classifier chain approach where multiple classifiers are joined in a chain and the predictions from all the preceding classifiers are used by the conceding classifiers to make the final prediction. Because of the multi-label multi-class data, macro accuracy, Hamming loss, and α-evaluation score are used to evaluate the performance. Results suggest that extreme gradient boosting (XGB) produces the best scores with a 92% α-evaluation score and a 84% macro accuracy score. The performance of XGB is much better than state-of-the-art approaches, in terms of both performance and computational complexity.

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Case Based Reasoning using K-Nearest Neighbor with Euclidean Distance for Early Diagnosis of Personality Disorder

Cited by 3 publications

References 7 publications

Heart failure survival prediction using novel transfer learning based probabilistic features

Heart failure survival prediction using novel transfer learning based probabilistic features

3D Facial Expression Recognition Based on Geometric Feature Fusion

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach

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