A new Log-Lomax distribution, properties, stock price, and heart attack predictions using machine learning techniques

Aliyu Ismail Ishaq; Abdullahi Ubale Usman; Hana N. Alqifari; Amani Almohaimeed; Hanita Daud; Sani Isah Abba; Ahmad Abubakar Suleiman; Aliyu Ismail Ishaq; Abdullahi Ubale Usman; Hana N. Alqifari; Amani Almohaimeed; Hanita Daud; Sani Isah Abba; Ahmad Abubakar Suleiman

doi:10.3934/math.2025575

AIMS Mathematics

2025, Volume 10, Issue 5: 12761-12807. doi: 10.3934/math.2025575

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A new Log-Lomax distribution, properties, stock price, and heart attack predictions using machine learning techniques

1.
Department of Statistics, Ahmadu Bello University, Zaria, Nigeria
2.
Department of Statistics, Aliko Dangote University of Science and Technology, Wudil, Nigeria
3.
Department of Statistics and Operation Research, College of Science, Qassim University, Saudi Arabia
4.
Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia
5.
Department of Civil Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia

Received: 17 February 2025 Revised: 24 April 2025 Accepted: 14 May 2025 Published: 30 May 2025
MSC : 60E05, 62F10, 62H12

In this study, we introduced the log-Lomax distribution, a more versatile probabilistic model for capturing various statistical properties. The study was divided into two sections: Modeling stock price exchange rates with the proposed log-Lomax distribution and incorporating log-Lomax features into machine learning models for prediction. In the modeling section, we introduced the log-Lomax distribution, which employed a logarithmic transformation with an exponent parameter. The model was left- and right-skewed, monotonic, inverted, and bathtub-shaped. Some properties were obtained, and several parameter estimation techniques were evaluated using a simulation study. The model was applied to two Nigerian stock exchange rate datasets: Naira-to-Euro and Naira-to-Riyal, as well as the Worcester heart attack patient dataset. The prediction section used insights from modeling methods and machine learning workflows to improve accuracy and reduce overfitting. The predictions were evaluated in two ways: With raw data and features derived from the log-Lomax model. Employing log-Lomax model features, Random Forest, and XGBoost achieved 99.87% accuracy in the Euro dataset, respectively. Random Forest and XGBoost had accuracy rates of 98.67% and 99.33% on the Riyal dataset, respectively, and 91.25% and 88.75% on the heart attack dataset. Random Forests and XGBoost are the preferred models, as they consistently provide the best prediction performance and stability mix across datasets.
- machine learning,
- artificial intelligence,
- statistical model,
- lomax distribution,
- stability,
- log-Lomax distribution,
- stock market
Citation: Aliyu Ismail Ishaq, Abdullahi Ubale Usman, Hana N. Alqifari, Amani Almohaimeed, Hanita Daud, Sani Isah Abba, Ahmad Abubakar Suleiman. A new Log-Lomax distribution, properties, stock price, and heart attack predictions using machine learning techniques[J]. AIMS Mathematics, 2025, 10(5): 12761-12807. doi: 10.3934/math.2025575

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Abstract

In this study, we introduced the log-Lomax distribution, a more versatile probabilistic model for capturing various statistical properties. The study was divided into two sections: Modeling stock price exchange rates with the proposed log-Lomax distribution and incorporating log-Lomax features into machine learning models for prediction. In the modeling section, we introduced the log-Lomax distribution, which employed a logarithmic transformation with an exponent parameter. The model was left- and right-skewed, monotonic, inverted, and bathtub-shaped. Some properties were obtained, and several parameter estimation techniques were evaluated using a simulation study. The model was applied to two Nigerian stock exchange rate datasets: Naira-to-Euro and Naira-to-Riyal, as well as the Worcester heart attack patient dataset. The prediction section used insights from modeling methods and machine learning workflows to improve accuracy and reduce overfitting. The predictions were evaluated in two ways: With raw data and features derived from the log-Lomax model. Employing log-Lomax model features, Random Forest, and XGBoost achieved 99.87% accuracy in the Euro dataset, respectively. Random Forest and XGBoost had accuracy rates of 98.67% and 99.33% on the Riyal dataset, respectively, and 91.25% and 88.75% on the heart attack dataset. Random Forests and XGBoost are the preferred models, as they consistently provide the best prediction performance and stability mix across datasets.

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