# Bridging the Gap in Bangla Healthcare: Machine Learning Based Disease Prediction Using a Symptoms-Disease Dataset

arXiv: https://arxiv.org/abs/2601.12068 · HTML: https://mlsanity.com/paper/2601.12068/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus](https://mlsanity.com/paper/1902.10028/) (2019)

## Built on by (most cited first, 4 of 4)

- [StanceMoE: Mixture-of-Experts Architecture for Stance Detection](https://mlsanity.com/paper/2604.00878/) (2026)
- [BiST: A Gold Standard Bangla-English Bilingual Corpus for Sentence Structure and Tense Classification with Inter-Annotator Agreement](https://mlsanity.com/paper/2604.04708/) (2026)
- [Towards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach](https://mlsanity.com/paper/2607.01115/) (2026)
- [BanglaMed-QA: A Question Answering System for Healthcare Support in Bangla](https://mlsanity.com/paper/2608.28329/) (2026)

## Abstract (arXiv)

Increased access to reliable health information is essential for non-English-speaking populations, yet resources in Bangla for disease prediction remain limited. This study addresses this gap by developing a comprehensive Bangla symptoms-disease dataset containing 758 unique symptom-disease relationships spanning 85 diseases. To ensure transparency and reproducibility, we also make our dataset publicly available. The dataset enables the prediction of diseases based on Bangla symptom inputs, supporting healthcare accessibility for Bengali-speaking populations. Using this dataset, we evaluated multiple machine learning models to predict diseases based on symptoms provided in Bangla and analyzed their performance on our dataset. Both soft and hard voting ensemble approaches combining top-performing models achieved 98% accuracy, demonstrating superior robustness and generalization. Our work establishes a foundational resource for disease prediction in Bangla, paving the way for future advancements in localized health informatics and diagnostic tools. This contribution aims to enhance equitable access to health information for Bangla-speaking communities, particularly for early disease detection and healthcare interventions.