Integrated Machine Learning Model for Multi-Disease Comorbidity Prediction

Maria Nayab; Ahmad Ibrahim

Integrated Machine Learning Model for Multi-Disease Comorbidity Prediction

Maria Nayab^*
, Ahmad Ibrahim

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The growing prevalence of lifestyle diseases such as obesity, diabetes, heart, liver, and sleep disorders highlight the need for intelligent healthcare solutions. These illnesses often coexist, complicating diagnosis and prevention. This study presents a unified machine learning model trained on five heterogeneous health datasets to predict comorbid disease risks. After preprocessing and encoding, Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost algorithms were applied. XGBoost achieved the highest accuracies across most diseases: Obesity (96%), Diabetes (74%), Heart Disease (88%), Liver Disease (90%), and Sleep Apnea (96%). Critical feature analysis identified age, BMI, blood pressure, and physical activity as common predictors. The model supports early detection and integrated healthcare decision-making.

Original language	English
Title of host publication	KSII The 17th International Conference on Internet (ICONI) 2025
Place of Publication	Okinawa, Japan
Publisher	Korean Society for Internet Information
Number of pages	3
Publication status	Published - 14 Dec 2025
Event	17th International Conference on Internet (ICONI 2025) - Okinawa, Japan, Okinawa, Japan Duration: 14 Dec 2025 → 17 Dec 2025 https://iconi.org/

Conference

Conference	17th International Conference on Internet (ICONI 2025)
Abbreviated title	ICONI 2025
Country/Territory	Japan
City	Okinawa
Period	14/12/25 → 17/12/25
Internet address	https://iconi.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

@inproceedings{a7954aaf9762462dad78f6ed27637ca0,

title = "Integrated Machine Learning Model for Multi-Disease Comorbidity Prediction",

abstract = " The growing prevalence of lifestyle diseases such as obesity, diabetes, heart, liver, and sleep disorders highlight the need for intelligent healthcare solutions. These illnesses often coexist, complicating diagnosis and prevention. This study presents a unified machine learning model trained on five heterogeneous health datasets to predict comorbid disease risks. After preprocessing and encoding, Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost algorithms were applied. XGBoost achieved the highest accuracies across most diseases: Obesity (96\%), Diabetes (74\%), Heart Disease (88\%), Liver Disease (90\%), and Sleep Apnea (96\%). Critical feature analysis identified age, BMI, blood pressure, and physical activity as common predictors. The model supports early detection and integrated healthcare decision-making.",

author = "Maria Nayab and Ahmad Ibrahim",

year = "2025",

month = dec,

day = "14",

language = "English",

booktitle = "KSII The 17th International Conference on Internet (ICONI) 2025",

publisher = "Korean Society for Internet Information",

note = "17th International Conference on Internet (ICONI 2025) , ICONI 2025 ; Conference date: 14-12-2025 Through 17-12-2025",

url = "https://iconi.org/",

}

TY - GEN

T1 - Integrated Machine Learning Model for Multi-Disease Comorbidity Prediction

AU - Nayab, Maria

AU - Ibrahim, Ahmad

PY - 2025/12/14

Y1 - 2025/12/14

N2 - The growing prevalence of lifestyle diseases such as obesity, diabetes, heart, liver, and sleep disorders highlight the need for intelligent healthcare solutions. These illnesses often coexist, complicating diagnosis and prevention. This study presents a unified machine learning model trained on five heterogeneous health datasets to predict comorbid disease risks. After preprocessing and encoding, Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost algorithms were applied. XGBoost achieved the highest accuracies across most diseases: Obesity (96%), Diabetes (74%), Heart Disease (88%), Liver Disease (90%), and Sleep Apnea (96%). Critical feature analysis identified age, BMI, blood pressure, and physical activity as common predictors. The model supports early detection and integrated healthcare decision-making.

AB - The growing prevalence of lifestyle diseases such as obesity, diabetes, heart, liver, and sleep disorders highlight the need for intelligent healthcare solutions. These illnesses often coexist, complicating diagnosis and prevention. This study presents a unified machine learning model trained on five heterogeneous health datasets to predict comorbid disease risks. After preprocessing and encoding, Random Forest, Logistic Regression, Support Vector Machine (SVM), and XGBoost algorithms were applied. XGBoost achieved the highest accuracies across most diseases: Obesity (96%), Diabetes (74%), Heart Disease (88%), Liver Disease (90%), and Sleep Apnea (96%). Critical feature analysis identified age, BMI, blood pressure, and physical activity as common predictors. The model supports early detection and integrated healthcare decision-making.

M3 - Conference contribution

BT - KSII The 17th International Conference on Internet (ICONI) 2025

PB - Korean Society for Internet Information

CY - Okinawa, Japan

T2 - 17th International Conference on Internet (ICONI 2025)

Y2 - 14 December 2025 through 17 December 2025

ER -

Integrated Machine Learning Model for Multi-Disease Comorbidity Prediction

Abstract

Conference

UN SDGs

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