Application of Improved Decision Tree C4.5 Algorithms in the Judgment of Diabetes Diagnostic Effectiveness

Liu, Jiawei; Ning, Bo; Shi, Daosheng

doi:10.1088/1742-6596/1237/2/022116

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…In general, the C4.5 algorithm for building a decision tree is as follows: Select an attribute as the root, create branches for each value, divide the cases into branches, and repeat the process for each branch until all cases on the branch have the same class. The C4.5 algorithm continues to be developed to improve its efficiency in handling complex and large data (Liu et al, 2019).…”

Section: Data Processing and Analysismentioning

confidence: 99%

Analysis of Land Plot Sales Using the C4.5 Algorithm in Property and Housing Sector

Syahrin

2024

ISE

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This research examines the application of the C4.5 algorithm in analyzing land plot sales data at PT Piliruma Rosa Land. Using data from two main projects, namely Harmoni Farm House and Nuansa Alam Agroewisata and Luxury, this study aims to build a decision tree model that is effective in predicting consumer interest. The analysis was conducted by evaluating the contribution of various sales attributes, specifically Sold Area and Number of Sold Lots, which proved to have a significant impact on the classification of buyer interest. The results of the model showed an accuracy rate of 91.18%, signifying the effectiveness of the C4.5 algorithm in assisting strategic decision-making in the property sector. The findings provide important insights for real estate developers in optimizing marketing and sales strategies, while offering a reliable analytical method for similar sectors. Penelitian ini mengkaji penerapan algoritma C4.5 dalam menganalisis data penjualan tanah kavling pada PT Piliruma Rosa Land. Melalui data dari dua proyek utama, yaitu Harmoni Farm House dan Nuansa Alam Agroewisata dan Luxury, penelitian ini bertujuan untuk membangun model pohon keputusan yang efektif dalam memprediksi minat konsumen. Analisis dilakukan dengan mengevaluasi kontribusi dari berbagai atribut penjualan, khususnya luas terjual dan jumlah kavling terjual, yang terbukti memberikan dampak signifikan terhadap klasifikasi minat pembeli. Hasil dari model ini menunjukkan tingkat akurasi sebesar 91.18%, menandakan efektivitas algoritma C4.5 dalam membantu pengambilan keputusan strategis di sektor properti. Temuan ini memberikan wawasan penting bagi pengembang real estat dalam mengoptimalkan strategi pemasaran dan penjualan, sekaligus menawarkan metode analitis yang bisa diandalkan untuk sektor serupa.

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Section: Data Processing and Analysismentioning

confidence: 99%

Analysis of Land Plot Sales Using the C4.5 Algorithm in Property and Housing Sector

Syahrin

2024

ISE

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“…The decision tree represents the data in a way that is easily understandable. The decision tree is feasible in a way that it can handle a large set of data and tackles both numerical and categorical variables [52][53][54][55][56]. The decision tree consists of a variety of algorithms such as C4.5, Iterative Dichotomiser 3 (ID3), Chi-Squared Automatic Interaction Detection (CHAID), Classification and Regression Tree (CART), etc., and each algorithm is based on a specific mechanism.…”

Section: Introductionmentioning

confidence: 99%

Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant

et al. 2021

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The thirst of the Earth for energy is lurching towards catastrophe in an era of increasing water shortage where most of the power plants are hydroelectric. The hydro-based power systems are facing challenges in determining day-ahead generation schedules of cascaded hydropower plants. The objective of the current study is to find a speedy and practical method for predicting and classifying the future schedules of hydropower plants in order to increase the overall efficiency of energy by utilizing the water of cascaded hydropower plants. This study is significant for water resource planners in the planning and management of reservoirs for generating energy. The proposed method consists of data mining techniques and approaches. The energy production relationship is first determined for upstream and downstream hydropower plants by using multiple linear regression. Then, a cluster analysis is used to find typical generation curves with the help of historical data. The decision tree algorithm C4.5, Iterative Dichotomiser 3-IV, improved C4.5 and Chi-Squared Automatic Interaction Detection are adopted to quickly predict generation schedules, and detailed comparison among different algorithms are made. The decision tree algorithms are solved using SIPINA software. Results show that the C4.5 algorithm is more feasible for rapidly generating the schedules of cascaded hydropower plants. This decision tree algorithm is helpful for the researchers to make fast decisions in order to enhance the energy production of cascaded hydropower plants. The major elements of this paper are challenges and solution of head sensitive hydropower plants, using the decision-making algorithms for producing the generation schedules, and comparing the generation from the proposed method with actual energy production.

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Evolving Deep Recurrent Neural Networks Using A New Variable-Length Genetic Algorithm

Viswambaran

Chen

Xue

et al. 2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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Application of Improved Decision Tree C4.5 Algorithms in the Judgment of Diabetes Diagnostic Effectiveness

Cited by 3 publications

References 1 publication

Analysis of Land Plot Sales Using the C4.5 Algorithm in Property and Housing Sector

Analysis of Land Plot Sales Using the C4.5 Algorithm in Property and Housing Sector

Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant

Evolving Deep Recurrent Neural Networks Using A New Variable-Length Genetic Algorithm

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