Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians.

Background: Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population.

Objective: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores.

Methods: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort.

Paediatric orthopaedic expert system.

The paediatric orthopaedic expert system analyses and predicts the healing time of limb fractures in children using machine learning. As far we know, no published research on the paediatric orthopaedic expert system that predicts paediatric fracture healing time using machine learning has been published. The University Malaya Medical Centre (UMMC) offers paediatric orthopaedic data, comprises children under the age of 12 radiographs limb fractures with ages recorded from the date and time of initial trauma.

Validation of the general Framingham Risk Score (FRS), SCORE2, revised PCE and WHO CVD risk scores in an Asian population.

Background: Cardiovascular risk prediction models incorporate myriad CVD risk factors. Current prediction models are developed from non-Asian populations, and their utility in other parts of the world is unknown. We validated and compared the performance of CVD risk prediction models in an Asian population.

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm.

Background: Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients.

Objective: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.

Methods: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017.

In-hospital risk stratification algorithm of Asian elderly patients.

Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients).