Automating Leukemia Diagnosis with Autoencoders: A Comparative Study

Leukemia is one of the most common and death-threatening types of cancer that threaten human life. Medical data from some of the patient's critical parameters contain valuable information hidden among these data. On this subject, deep learning can be used to extract this information. In this paper, AutoEncoders have been used to develop valuable features to help the precision of leukemia diagnosis. It has been attempted to get the best activation function and optimizer to use in AutoEncoder and designed the best architecture for this neural network. The proposed architecture is compared with this area's classical machine learning models. Our proposed method performs better than other machine learning in precision and f1-score metrics by more than 11%

A comparative analysis of classifiers in cancer prediction using multiple data mining techniques

In recent years, application of data mining methods in health industry has received increased attention from both health professionals and scholars. This paper presents a data mining framework for detecting breast cancer based on real data from one of Iran hospitals by applying association rules and the most commonly used classifiers. The former were adopted for reducing the size of datasets, while the latter were chosen for cancer prediction. A k-fold cross validation procedure was included for evaluating the performance of the proposed classifiers. Among the six classifiers used in this paper, support vector machine achieved the best results, with an accuracy of 93%. It is worth mentioning that the approach proposed can be applied for detecting other diseases as well

SMOOTH SUPPORT VECTOR MACHINE DAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE UNTUK MENDIAGNOSIS KANKER PAYUDARA

Kanker payudara merupakan kanker yang paling umum menyerang wanita dan menjadi kanker penyebab kematian utama bagi wanita di seluruh dunia. Penyebab dari kanker payudara masih belum dapat dipastikan sehingga metode preventif yang spesifik untuk penyakit ini juga belum dapat ditentukan, oleh karena itu diagnosis terhadap kanker payudara sedini mungkin menjadi sangat penting bagi para dokter dan tenaga medis untuk menyelamatkan pasien maupun orang-orang yang memiliki faktor risiko kanker payudara. Beberapa penelitian telah dikembangkan dengan ide dasar mengklasifikasikan kanker payudara berdasarkan rekaman gambar radiologi dan usia pasien terhadap hasil biopsi. Berdasarkan keunggulan smooth SVM (SSVM) serta potensi MARS dalam menyelesaikan permasalahan diagnosis kanker payudara, tulisan ini mengkaji dan memaparkan kedua metode tersebut digunakan untuk mengklasifikasikan kanker payudara ke dalam dua kelompok yaitu kelompok malignant dan kelompok benign. Secara umum baik SSVM maupun MARS mampu menghasilkan tingkat akurasi yang sama-sama tinggi. Tingkat akurasi kedua metode dalam mendiagnosis kanker payudara ke dalam kelompok benign dan malignant yang cukup tinggi dipercaya dapat mendukung prosedur pemeriksaan dan diagnosis kanker payudara.Kata Kunci : kanker payudara, klasifikasi, smooth SVM, MAR

Information and Control ICIC International c ⃝2011 ISSN

Abstract. The statistical process control (SPC) chart is effective in detecting proces

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models

مقایسه مدل شبکه عصبی مصنوعی و درخت تصمیم برای شناسایی و پیش بینی عوامل مرتبط با دیابت نوع2

هدف:  یکی از اهداف تحقیقات پزشکی تعیین عوامل مرتبط در پیش ­بینی بیماری می ­باشد. یکی از شایع ­ترین بیماری های متابولیک در ایران، دیابت می­باشد. هدف از این مطالعه شناسایی عوامل موثر در پیش بینی دیابت با استفاده از مدل­ های شبکه عصبی مصنوعی و درخت تصمیم می ­باشد. روش بررسی:  برای انجام مطالعه، پرونده 901 تن از افرادی که در سال­ های 91 و 92 به مراکز بهداشتی شهر مشهد مراجعه کرده بودند، استفاده گردید. در ابتدا با استفاده از روش­ های آمار توصیفی و تحلیلی، داده ­ها آنالیز شدند. سپس 70% داده­ ها به طور تصادفی برای ساخت مدل­ های شبکه عصبی مصنوعی و درخت تصمیم انتخاب شدند. 30% باقیمانده برای مقایسه عملکرد مدل ­ها استفاده شد. در نهایت عملکرد مدل ­ها با استفاده از سطح زیر منحنی راک (ROC) مورد مقایسه قرار گرفت. یافته ­ها:  توسعه دو مدل پیش ­بینی با استفاده از 14 متغیر انجام شد. دو مدل از نظر سطح زیر منحنی راک، حساسیت، ویژگی و صحت مورد ارزیابی قرار گرفتند. برای مدل شبکه عصبی، سطح زیر منحنی راک و حساسیت به ترتیب 69/1 و 74/2 بدست آمد. برای مدل درخت تصمیم نیز سطح زیر منحنی راک و حساسیت به ترتیب 68/9 و 64/77  بدست آمد. در هر دو مدل متغیرهای سابقه خانوادگی دیابت، تری گلیسرید، شاخص توده بدنی، لیپوپروتئین با چگالی کم و فشار خون سیستولیک و دیاستولیک مهم ترین عوامل مرتبط در شناسایی دیابت نوع 2 بودند.      نتیجه‌ گیری:  نتایج نشان داد که مدل شبکه عصبی چند لایه سطح زیر منحنی راک بهتری نسبت به درخت تصمیم CART در پیش ­بینی دیابت نوع 2 دارد. همچنین لیپوپروتئین با چگالی کم مهم ­ترین عوامل مرتبط در شناسایی دیابت نوع 2 می ­باشد. مطالعه حاکی از آنست که روش ­های داده­کاوی نوین از جمله شبکه عصبی مصنوعی و درخت تصمیم می ­توانند برای شناسایی عوامل مرتبط با بیماری­ ها مورد استفاده قرار گیرند

Hybrid Artificial Neural Networks Modeling for Faults Identification of a Stochastic Multivariate Process

Due to the recent rapid growth of advanced sensing and production technologies, the monitoring and diagnosis of multivariate process operating performance have drawn increasing interest in process industries. The multivariate statistical process control (MSPC) chart is one of the most commonly used tools for detecting process faults. However, an out-of-control MSPC signal only indicates that process faults have intruded the underlying process. Identifying which of the monitored quality variables is responsible for the MSPC signal is fairly difficult. Pinpointing the responsible variable is vital for process improvement because it effectively determines the root causes of the process faults. Accordingly, this identification has become an important research issue concerning recent multivariate process applications. In contrast with the traditional single classifier approach, the present study proposes hybrid modeling schemes to address problems that involve a large number of quality variables in a multivariate normal process. The proposed scheme includes multivariate adaptive regression splines (MARS), logistic regression (LR), and artificial neural network (ANN). By applying MARS and LR techniques, we may obtain fewer but more significant quality variables, which can serve as inputs to the ANN classifier. The performance of our proposed approaches was evaluated by conducting a series of experiments

Hybrid ABC optimized MARS-based modeling of the milling tool wear from milling run experimental data

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed