Research Article
Analysis and Prediction of Soil Fertility Using Machine Learning Techniques
Tigist Mintesnot Tewabe*
Issue:
Volume 1, Issue 3, September 2026
Pages:
73-83
Received:
6 March 2026
Accepted:
28 July 2026
Published:
17 August 2026
Abstract: Soil fertility is a crucial aspect of agricultural productivity and sustainability, determines the soil's capacity to provide essential nutrients necessary for plant growth and development. This study focuses on the analysis and prediction of soil fertility using ensemble learning techniques in the South Gondar Zone. By examining various soil parameters, including macro and micronutrients, soil structure, pH, and organic matter content, the research aims to develop predictive models that accurately assess soil fertility levels. The objective of this study is to analyze and predict soil fertility using machine learning techniques, specifically targeting the south Gondar Zone, in the Amhara region of Ethiopia. The dataset for this study comprises 20,168 instances, including both fertile and non-fertile samples, with 17 selected attributes. Several machine learning models were evaluated on both the original and SMOTE-balanced datasets. The models included Random Forest, AdaBoost, and XGBoost classifiers. I applied Random Forest classifier consistently demonstrated the highest performance, with testing accuracies of 94.55% on the original dataset and 94.37% on the SMOTE-balanced dataset. AdaBoost also showed strong performance, achieving testing accuracies of 94.6% and 94.31% on the original and SMOTE-balanced datasets, respectively. XGBoost performed well but was slightly less accurate compared to the ensemble methods. However, XGBoost showed robust performance on both datasets, with testing accuracies of 94.04% and 94.27%. Feature importance analysis using the Random Forest classifier has shown that factors such as Clay, CEC, CaCO3, Sand, and Mn significantly impact soil fertility prediction. as a conclusion Random Forest classifier, an ensemble-based learning technique, was the most reliable and accurate model for predicting soil fertility. These results highlight the importance of specific soil properties in determining fertility and can guide targeted soil management practices to improve agricultural productivity.
Abstract: Soil fertility is a crucial aspect of agricultural productivity and sustainability, determines the soil's capacity to provide essential nutrients necessary for plant growth and development. This study focuses on the analysis and prediction of soil fertility using ensemble learning techniques in the South Gondar Zone. By examining various soil param...
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Research Article
TabTransformer-XGBoost Hybrid Framework for Gastric Cancer Classification Using Tabular Clinical Data with SHAP-Based Explainability
Issue:
Volume 1, Issue 3, September 2026
Pages:
84-104
Received:
19 September 2026
Accepted:
28 September 2026
Published:
20 October 2026
DOI:
10.11648/j.ajris.20260103.12
Downloads:
Views:
Abstract: Gastric cancer remains an important health challenge, and accurate classification using routinely available clinical information could support timely clinical decision-making. However, conventional machine learning approaches may have difficulty capturing complex relationships among heterogeneous clinical variables, while highly predictive models may provide limited insight into how individual features influence their decisions. This study proposes an explainable TabTransformer-XGBoost hybrid framework for gastric cancer classification using structured clinical data. A publicly available gastric cancer dataset containing 212,354 patient records and 29 attributes was used. The proposed framework incorporates data cleaning, feature selection using mutual information, stratified data partitioning, SMOTE-based class balancing, contextual feature learning with TabTransformer, XGBoost classification, and SHAP-based explainability. The framework was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC and compared with conventional machine learning models, including Decision Tree, Random Forest, XGBoost, Naïve Bayes, Logistic Regression, and Multilayer Perceptron. The proposed framework achieved 90.13% accuracy, 86.17% precision, 90.13% recall, and an F1-score of 85.46%, outperforming the evaluated baseline models in the principal classification measures. Ablation analysis showed that XGBoost provided strong predictive performance, while the TabTransformer contributed contextual feature representations within the hybrid architecture. SHAP analysis further identified influential learned representations and demonstrated nonlinear relationships involving clinical variables such as age and alcohol consumption, thereby providing greater transparency into model predictions. The findings indicate that the proposed framework provides a promising basis for explainable gastric cancer classification from tabular clinical data, although independent multi-centre and real-world clinical validation are required before clinical deployment.
Abstract: Gastric cancer remains an important health challenge, and accurate classification using routinely available clinical information could support timely clinical decision-making. However, conventional machine learning approaches may have difficulty capturing complex relationships among heterogeneous clinical variables, while highly predictive models m...
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