Diabetes Diagnosis Based on KNN

Ali, Ameer H.; Alrubei, Mohammed A. T.; Hassan, Laith Falah Mohammed; Al-Ja'afari, Mohannad A. M.; Abdulwahed, Saif H.

doi:10.31436/iiumej.v21i1.1206

Cited by 43 publications

(11 citation statements)

References 9 publications

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“…e main comparison models are SVM [21], Radial Basis Neural Network (RBFNN) [22], Random Forest (RF) [23], Iterative Dichotomiser 3 (ID3) [24], and K-Nearest Neighbor (KNN) [25], and nine teaching experts were asked to score 180 teachers during the experiment, and each expert scored 40. e final teaching quality score of each teacher was given by taking the average value. And according to the scores, the grade where they were located was determined as A to E. 90-100 was A, 80-89 was B, 70-79 was C, 60-69 was D, and 0-59 was E. e classification accuracy was used as the evaluation index for the performance of each algorithm.…”

Section: Experimental Discussionmentioning

confidence: 99%

Teaching Evaluation Method Based on Fuzzy Support Vector Machine Algorithm

Wang

Xu²,

Wang³

2022

Mobile Information Systems

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For schools, the quality of teaching and learning is an important guarantee for achieving educational goals. There are very many factors affecting teaching quality, including hard and soft factors. Among them, the soft factors mainly refer to the teaching quality of teachers and the teaching management mechanism of schools. The teaching quality of teachers is the most critical among all factors. Traditional teaching quality evaluation (TQE) mainly adopts the way of manual scoring. This way of TQE lacks data support, and there is no evaluation mechanism based on multiple data sources. Therefore, the TQE results obtained in this way are subjective and inaccurate. Considering that the main subjects of the whole teaching session are teachers, students, and schools, each subject generates massive information in the whole teaching process. In our study, a novel evaluation method based on fuzzy classification algorithm is proposed to be applied in teaching evaluation. Firstly, this paper collects the relevant data generated by the three subjects in the teaching process, respectively. Secondly, after removing the unsuitable data, principal component analysis is used to extract the main features of the applicable data. Finally, a fuzzy support vector machine (FSVM) is used to classify and analyze the feature data in order to derive each teacher’s teaching evaluation results. The final evaluation results in this paper are divided into five grades, i.e., five categories: excellent, good, moderate, passing, and failing. The comparison with other algorithms demonstrates that the teaching evaluation model based on fuzzy comprehensive evaluation algorithm used in this paper has the advantages of high evaluation accuracy and objectivity, and we hope that this study will be useful in the field of teaching evaluation.

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Section: Experimental Discussionmentioning

confidence: 99%

Teaching Evaluation Method Based on Fuzzy Support Vector Machine Algorithm

Wang

Xu²,

Wang³

2022

Mobile Information Systems

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“…Penelitian tentang klasifikasi dengan metode KNN juga digunakan dalam penelitian (Ali et al, 2020;Bah & Yu, 2021;Pamuji, 2021;Pratama et al, 2021;Rudiyan et al, 2022;Wahyono et al, 2020;Yunus et al, 2021). Dalam penelitian (Wahyono et al, 2020) digunakan data uji 10 %, p = 5 dan k bervariasi dari 3 sampai dengan 9, namun hanya menggunakan satu nilai random state yaitu 0.…”

Section: Hasil Dan Diskusiunclassified

Perbandingan Penggunaan Jarak Manhattan, Jarak Euclid, dan Jarak Minkowski dalam Klasifikasi Menggunakan Metode KNN pada Data Iris

Setiawan

2022

juses

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Dalam klasifikasi, ukuran kebaikan suatu metode ditentukan oleh akurasi dan skor F1. Penelitian ini bertujuan untuk menentukan rata-rata akurasi dan skor F1 pada klasifikasi menggunakan metode KNN pada data Iris dengan bantuan program Python. Rata-rata akurasi maupun skor F1 ditentukan dengan membuat variasi parameter yang digunakan yaitu random state dari 1 sampai dengan 10000, k yang digunakan dalam metode KNN dari 1 sampai dengan 31 (tergantung prosentase data uji yang digunakan), q (definisi jarak yang digunakan yaitu jarak Manhattan, jarak Euclid, dan jarak Minkowski) serta persentase data uji yang digunakan. Dalam penelitian ini, digunakan persentase data uji 10%, 20 %, 30 %, dan 40 %. Jika digunakan k = 5 dalam metode KNN, variasi random state 1 sampai dengan 10000 dan digunakan data uji 20 % maka diperoleh akurasi rata-rata 96,6667 %. Demikian juga dapat diperoleh histogram dari akurasi tersebut. Histogram tersebut cenderung menceng ke kiri. Dalam penelitian ini, juga diperoleh hasil bahwa dengan melakukan variasi k dalam metode KNN juga dapat diperoleh akurasi maupun skor F1 yaitu bahwa makin besar data uji yang digunakan maka makin kecil nilai k untuk mencapai akurasi ataupun skor F1 maksimum. Lebih lanjut, dalam perubahan nilai p pada jarak Minkowski, apabila p membesar maka nilai maksimum rata-rata akurasi akan cenderung membesar namun nilai k cenderung mengecil.

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“…It is a technique for classifying unknown cases by searching for the nearby patterns in the pattern space [28] [29]. Euclidean distance is used by KNN to predict class:…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

The Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage

Anasanti

Hilyati

Novtariany

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Predicting early Type 2 diabetes (T2D) is critical for improved care and better T2D outcomes. An accurate and efficient T2D prediction relies on unbiased relevant features. In this study, we searched for important features to predict T2D by integrating ML-based models for feature selection and classification from 520 individuals newly diagnosed with diabetes or who will develop it. We used standard machine learning classifications, such as logistic regression (LR), Gaussian naive Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM) with linear basis function, and k-nearest neighbors (KNN). We set out to systematically explore the viability of main feature selection representing each different technique, such as a statistical filter method (F-score), an entropy-based filter method (mutual information), an ensemble-based filter method (random forest importance), and a stochastic optimization (simultaneous perturbation feature selection and ranking (SpFSR)). We used a stratified 10-fold cross-validation technique and assessed the performance of discrimination, calibration, and clinical utility. We attained the highest accuracy of 98% using RF with the full set of features (16 features), then used RF as a classifier wrapper to select the important features. We observed a combination of SpFSR and RF as the best model with a P-value above 0.05 (P-value = 0.26), statistically attaining the same accuracy as the full features. The study's findings support the efficiency and usefulness of the suggested method for choosing the most important features of diabetic data: polyuria, gender, polydipsia, age, itching, sudden weight loss, delayed healing, and alopecia.

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Diabetes Diagnosis Based on KNN

Cited by 43 publications

References 9 publications

Teaching Evaluation Method Based on Fuzzy Support Vector Machine Algorithm

Teaching Evaluation Method Based on Fuzzy Support Vector Machine Algorithm

Perbandingan Penggunaan Jarak Manhattan, Jarak Euclid, dan Jarak Minkowski dalam Klasifikasi Menggunakan Metode KNN pada Data Iris

The Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage

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