Heart Disease Classification Model Using K-Nearest Neighbor Algorithm

Rahman, Ben; Warnars, Harco Leslie Hendric Spits; Sabarguna, Boy Subirosa; Budiharto, Widodo

doi:10.1109/icic54025.2021.9632918

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…The five abnormal gaits were mainly determined by different types of gait behavioral disorders. In our experiment, we used five recognition methods, i.e., Gradient Boosting (GB), KNeighbors (KN), Multilayer Perception (MLP), Random Forest (RF), and SVM, to classify the six gait features [28][29][30][31][32][33]. The parameter settings of the machine learning model obtained by GridSearch are shown in Table 3.…”

Section: Discussionmentioning

confidence: 99%

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A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

Zhao

Wang

et al. 2022

Sensors

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The recognition of abnormal gait behavior is important in the field of motion assessment and disease diagnosis. Currently, abnormal gait behavior is primarily recognized by pressure and inertial data obtained from wearable sensors. However, the data drift and wearing difficulties for patients have impeded the application of these wearable sensors. Here, we propose a contactless abnormal gait behavior recognition method that captures human pose data using a monocular camera. A lightweight OpenPose (OP) model is generated with Depthwise Separable Convolution to recognize joint points and extract their coordinates during walking in real time. For the walking data errors extracted in the 2D plane, a 3D reconstruction is performed on the walking data, and a total of 11 types of abnormal gait features are extracted by the OP model. Finally, the XGBoost algorithm is used for feature screening. The final experimental results show that the Random Forest (RF) algorithm in combination with 3D features delivers the highest precision (92.13%) for abnormal gait behavior recognition. The proposed scheme overcomes the data drift of inertial sensors and sensor wearing challenges in the elderly while reducing the hardware requirements for model deployment. With excellent real-time and contactless capabilities, the scheme is expected to enjoy a wide range of applications in the field of abnormal gait measurement.

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Section: Discussionmentioning

confidence: 99%

“…XGBoost is used as a key machine learning algorithm for feature importance ranking, which can eliminate unfavorable features of machine learning models [27][28][29]. As shown in Table 2, by reducing the low-scoring features in order of importance scores, we obtained the acceptance scores for different numbers of features.…”

Section: Feature Screeningmentioning

confidence: 99%

A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

Zhao

Wang

et al. 2022

Sensors

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“…The KNN works based on the principle that data is arranged in space based on specific features. When making a prediction based on new data the algorithm will provide a regression output based on the proximity to modelled data points [14]. In this case in order to capture higher complexities in the data a value of 25 neighbours was used.…”

Section: B Overview Of Machine Learning Algorithmsmentioning

confidence: 99%

Modelling Virtual Sensors for Indoor Environments with Machine Learning

Polanski

Angelopoulos

2022

2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)

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Virtual Sensors model the sensing operation of physical sensors deployed in an area of interest by generating sensory data with accuracy and precision close to those collected by physical sensors. Their use in applications such as augmenting the infrastructure of IoT facilities and test beds, monitoring and calibrating the operation of physical sensors, and developing Digital Twins of physical systems have led virtual sensors to attract research attention. Machine learning provides methods for modelling patterns in complex and big data generated by IoT sensing devices, allowing to model the behaviour of these devices. In this work, we investigate ML methods as means of implementation for virtual sensors. In particular, we evaluate the performance of six ML methods in terms of their effectiveness, accuracy and precision in generating sensory data based on data from physical sensors. In our study, we use a multi-modal dataset comprising IoT sensory data for temperature, humidity and illumination collected over a period of two years in an office space at University of Geneva. Our results show that the best performing model at predicting an output of a missing sensor is the Random Forest method, achieving MAPE error below 3%, 5% and 18% respectively for temperature, humidity and illuminance. The worst performing models were the linear radial basis function neural network and linear regression. In future research, we plan to deploy the best performing models natively on IoT devices, making use of tinyML and extreme edge computing methods.

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“…The heart will never stop beating, even when sleeping. Humans cannot regulate their heart rate when the heart pumps blood [2]. Diagnosing heart failure requires a thorough evaluation of symptoms, medical history, and the results of a physical examination and medical tests.…”

Section: Introductionmentioning

confidence: 99%

K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset

Masitha,

Biddinika,

Herman

2023

matrik

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Heart failure is included in the category of cardiovascular disease. Heart disease is not easy to detect, and its detection needs to be done by experienced and skilled medical professionals. Most patients with heart failure require hospitalization. Common symptoms of heart disease, such as chest pain and high or low blood pressure, vary from person to person. This study aims to find the most optimal k value based on the accuracy obtained based on calculations by testing different k values, namely 1, 3, 5, 7, and 9. After getting the results of the accuracy of the five k values, compare which accuracy has the highest value, best for K-Nearest Neighbor (K-NN) models. The classification process uses the K-NN algorithm. This algorithm is quite easy to use because some parameters work using distance metrics and k values. Therefore, the value of k in the K-NN algorithm greatly affects the accuracy that will be produced. In the results of this study, the accuracy obtained was k = 7 and k = 9, which are the most optimal results because they have the highest accuracy compared to other k values, with an accuracy of 88%. The expected benefit of this research is that it can make a scientific contribution to research in the field of machine learning classification, especially in predicting heart failure

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Heart Disease Classification Model Using K-Nearest Neighbor Algorithm

Cited by 6 publications

References 4 publications

A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement

Modelling Virtual Sensors for Indoor Environments with Machine Learning

K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset

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