DETEKSI DAN PREDIKSI PENYAKIT DIABETES MELITUS TIPE 2 MENGGUNAKAN MACHINE LEARNING (SCOOPING REVIEW)

Diabetes Mellitus is a chronic disease and one of the non-communicable diseases whose growth is very fast. This study aims to explore and analyze the early detection and prediction system of risk factors for type 2 diabetes mellitus which utilizes machine learning methods. This type of research is a scoping review to accumulate and synthesize the results of previous studies on the early detection of risk factors and the prediction system of Diabetes Mellitus type 2 using machine learning methods. The inclusion criteria are articles in English or Indonesian, journals published in the 2017-2021 range, full text, and not systematic reviews. Article searches are 4 databases, namely Google Scholar, Pubmed, International Journal of Public Health Science/Hindawi, and IEEE Xplore.  The results obtained as many as 2,941 articles, using the PRISMA method. The remaining 15 studies were maintained and met the criteria for qualitative analysis. The articles used machine learning methods in the creation of early detection models and prediction systems. Some articles use the merging of two methods (statistical and machine learning). The machine learning techniques mostly use supervised, unsupervised, and deep learning techniques. For the algorithms used, the majority of researchers used more than one algorithm such as algorithm support vector machine (SVM), random forest (RF), Decision Tree (DT), LASSO, and others, to compare the best accuracy of each algorithm. Risk factors associated with Diabetes Mellitus type 2 incidence are age, gender, obesity, family history of the disease, lack of physical activity, genetics, environment, smoking, blood pressure, and diet

Data-driven based Optimal Feature Selection Algorithm using Ensemble Techniques for Classification

The shift in paradigm with advanced Machine Learning algorithms will help to face the challenges such as computational power, training time, and algorithmic stability. The individual feature selection techniques, hardly give the appropriate feature subsets, that might be vulnerable to the variations induced at the input data and thus led to wrong conclusions. An expedient technique should be designed for approximating the feature relevance to improve the performance for the data. Unlike the prevailing techniques, the novelty of the proposed Data-driven based Optimal Feature Selection (DOFS) algorithm is the optimal k-value ‘kf’ determined by the data for effective feature selection that minimizes the computational complexity and expands the prediction power using the gradient descent method. The experimental analysis of proposed algorithm is demonstarted with ensemble techniques for the non-communicable disease such as diabetes mellitus dataset produces an accuracy of 80.80%, whereas comparative performance analysis for benchmark dataset depicts the improved accuracy of 86.03%

Precision Medicine: Viable Pathways to Address Existing Research Gaps

Precision Medicine (PM) seeks to customize medical treatments for patients based on measurable and identifiable characteristics. Unlike personalized medicine, this effort is not intended to result in tailored care for each patient. Instead, this effort seeks to improve overall care within the medical domain by shifting the focus from one-size-fits-all care to optimized care for specified subgroups. In order for the benefits of PM to be expeditiously realized, the diverse skills sets of the scientific community must be brought to bear on the problem. This research effort explores the intersection of quality engineering (QE) and healthcare to outline how existing methodologies within the QE field could support existing PM research goals. Specifically this work examines how to determine the value of patient characteristics for use in disease prediction models with select machine learning algorithms, proposes a method to incorporate patient risk into treatment decisions through the development of performance functions, and investigates the potential impact of incorrect assumptions on estimation methods used in optimization models