Comparative analysis of thyroid disease and predict them using machine learning techniques

Veeranki, Sreenivasa Rao; Varshney, Manish Kumar

doi:10.53730/ijhs.v6ns3.8459

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…In [9], ML models are built using various preprocessing techniques to balance the unbalanced data and predicted using the Random Forest algorithm. In [10] In this work, we have developed the K-Nearest Neighbour model to diagnose and predict liver disease.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Liver Disease In Patients Using Logistic Regression of Machine Learning

Vardhan,

babu,

Raju

et al. 2024

Int. J. Res. Publ. Rev.

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The existence of people living without liver cancers is one of the basic considerations of human jobs. Subsequently, for better consideration, the discovery of liver sickness at a crude stage is important. For clinical specialists, foreseeing the disease in the beginning phases because of unobtrusive signs is an undeniably challenging undertaking. Many, when it is past the point of no return, the signs become obvious. The ebb and flow work plans to expand the apparent idea of liver infection utilizing AI strategies to settle this pandemic. The vital reason for the current work zeroed in on calculations for the characterization of sound individuals from liver datasets. Jogging on their prosperity factors, this exploration likewise means to contrast the grouping calculations and with give forecast exactness results.Choice Tree, Arbitrary Backwoods, Strategic Relapse, and Backing Vector Machine calculations are utilized to Foresee the sickness,by comparing accuracy rates, the goal of the paper is to predict liver disease and select the best machine-learning algorithms.finally found Logistic regression is best than support Vector Machine in terms of accurary 72%.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Liver Disease In Patients Using Logistic Regression of Machine Learning

Vardhan,

babu,

Raju

et al. 2024

Int. J. Res. Publ. Rev.

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show abstract

“…Penelitian ketiga dilakukan oleh [7] yang menganalisis perbandingan performa dari empat algoritma untuk klasifikasi penyakit hati yaitu algoritma Random Forest, MLP, K-Nearest Neighbor (KNN), dan SVM. Hasil penelitian menunjukkan algoritma MLP mendapatkan skor akurasi sebesar 68%, sedangkan algoritma SVM mendapatkan skor akurasi sebesar 74%.…”

Section: A Pendahuluanunclassified

Perbandingan Algoritma Support Vector Machine dan Neural Network untuk Klasifikasi Penyakit Hati

Nurrokhman

2023

ijcs

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Pada penelitian ini dilakukan perbandingan dua algoritma untuk klasifikasi penyakit hati, yaitu algoritma Support Vector Machine (SVM) dan Neural Network berjenis Multi Layer Perceptron (MLP). Pelatihan model dilakukan dengan bantuan Grid Search Cross Validation (GridSearchCV). Tujuan dari penelitian ini yaitu untuk mengetahui model machine learning dengan performa terbaik yang dihasilkan dari kedua algoritma tersebut. Dataset yang digunakan diambil dari situs UCI Machine Learning Repository dengan nama dataset yaitu Indian Liver Patient Dataset. Hasil penelitian menunjukkan bahwa model klasifikasi penyakit hati dengan algoritma SVM memiliki kinerja yang lebih baik dan akurasi yang lebih tinggi yaitu 87,65%. Kinerja yang baik ini juga ditandai dengan hasil pada Confusion Matrix yang menunjukkan bahwa model tidak memprediksi penderita penyakit hati sebagai bukan penderita penyakit hati sehingga tidak membahayakan penderita penyakit hati.

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Comparative analysis of thyroid disease and predict them using machine learning techniques

Cited by 2 publications

References 6 publications

Prediction of Liver Disease In Patients Using Logistic Regression of Machine Learning

Prediction of Liver Disease In Patients Using Logistic Regression of Machine Learning

Perbandingan Algoritma Support Vector Machine dan Neural Network untuk Klasifikasi Penyakit Hati

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