Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset

Uddin, Khandaker Mohammad Mohi; Ripa, Rokaiya; Yeasmin, Nilufar; Biswas, Nitish; Dey, Samrat Kumar

doi:10.1016/j.ibmed.2023.100100

Cited by 15 publications

(3 citation statements)

References 11 publications

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“…Karena itu, merupakan suatu keharusan untuk mengembangkan metode klasifikasi yang akurat dan efektif dalam proses diagnosis penyakit kardiovaskular. [3].…”

Section: Pendahuluanunclassified

Klasifikasi Cardiovascular Diseases Menggunakan Algoritma K-Nearest Neighbors (KNN)

Artanti,

Faisal,

Kurniawan

2024

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Penyakit Kardiovaskular (Cardiovascular Diseases) adalah faktor utama kematian global, dengan jumlah korban mencapai 17,9 juta jiwa setiap tahun atau sekitar 32% dari total kematian global (World Health Organization, 2021). Faktor risiko penyakit kardiovaskular diantaranya faktor usia, semakin bertambahnya usia seseorang, maka semakin tinggi risiko terkena penyakit kardiovaskular. Faktor lain yaitu memiliki riwayat penyakit kardiovaskular dalam keluarga, diabetes, tekanan darah tinggi, obesitas (kegemukan), Pola hidup tidak sehat, dan Stres. (Kemenkes RI, 2021). Masalah pada penelitian ini adalah bagaimana mengetahui model K-Nearest Neighbors (KNN) dengan baik melalui perhitungan accuracy, recall, precision, dan f1-score pada klasifikasi penyakit kardiovaskular. Tujuan penelitin ini adalah mengetahui kinerja K-Nearest Neighbors (KNN) dengan baik melalui perhitungan accuracy, recall, precision, dan f1-score pada klasifikasi penyakit kardiovaskular. Metode K-Nearest Neighbor (KNN) adalah salah satu metode klasifikasi yang memanfaatkan pola-pola data yang ada dalam dataset untuk mengklasifikasi kategori atau kelas dari suatu sampel yang belum diketahui. Hasil klasifikasi data pelatihan menunjukkan akurasi sebesar 85.49%, dengan precision 84,43%, recall 87,04%, dan f1-score 85,71%. Melalui uji coba menggunakan KNN, diperoleh hasil dengan akurasi sebesar 91% dan nilai presisi 90%, recall 93%, dan f1-score 92%. Kesimpulan dari penelitian ini adalah metode K-Nearest Neighbor (KNN) memiliki hasil yang baik untuk melakukan klasifikasi pada penyakit kardiovaskular yaitu akurasinya 91%.

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“…Karena itu, merupakan suatu keharusan untuk mengembangkan metode klasifikasi yang akurat dan efektif dalam proses diagnosis penyakit kardiovaskular. [3].…”

Section: Pendahuluanunclassified

Klasifikasi Cardiovascular Diseases Menggunakan Algoritma K-Nearest Neighbors (KNN)

Artanti,

Faisal,

Kurniawan

2024

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“…Identifying an appropriate feature subset for efficient classification is a difficult issue that requires much effort. A thorough search of the data set's sample space is required to get this result [20]. According to, increasingly contribute to the advancement of some developing results for a potentially successful framework for the detection of heart disease.…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Naeem,

Senapati,

Bhuva

et al. 2024

IEEE Access

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This research proposes a successful technique for identifying individuals using feature extraction methods and signal processing approaches. The novelty of this study is in the use of feature extraction methods in conjunction with other signal-processing approaches. While working with the sensors, an artificial neural network (ANN) technique is employed to identify the scent patterns that are present in persons. The numerous gases released by the human body are measured using ten different kinds of sensors, all of which are metal oxide semiconductors. Before using ANN patterns to generate patterns from sensor data, it is important to scan and extract sensory information from that data. Each participant is recognized and scanned for a totally of 1000 different characteristics during the course of the multiple investigations, which are conducted across a variety of time periods that include 5, 10, 15, and 20 people. Because of the varying time periods, signals from sensors are received in analog form, which is then transformed by Arduino into digital form. It is necessary to train an architecture on the data set that has been created. The benchmarks that are employed for the assessment of the model that is presented for the identification of human odor include sensitivity, f-measures, accuracy, and specificity, among other things. Experiments are carried out using the assessment measures, and the findings demonstrate that this model has an accuracy of greater than 85 % in most cases.

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“…Working with patient databases for patients with heart disease is a practical application. Therefore, it is reasonable to consider using the knowledge of diverse professionals compiled in databases to aid in the diagnosis process [ 15 ]. Every conventional model for assessing CVD risk implicitly assumes that every risk factor is linearly related to the CVD outcome [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

Hossain,

Hasan,

Faruk

et al. 2024

BMC Cardiovasc Disord

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Background Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs. Materials and methods The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve. Results Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989). Conclusion This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient’s CVD prognosis.

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Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset

Cited by 15 publications

References 11 publications

Klasifikasi Cardiovascular Diseases Menggunakan Algoritma K-Nearest Neighbors (KNN)

Klasifikasi Cardiovascular Diseases Menggunakan Algoritma K-Nearest Neighbors (KNN)

Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach

Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

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